Infographic_Visualization.ipynb

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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
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    "colab": {
      "name": "Infographic_Visualization.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "eca3c3a0"
      ]
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "2b36c13f-330d-4cfa-b3ff-7eb1a8b859f9"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "id": "2b36c13f-330d-4cfa-b3ff-7eb1a8b859f9",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16f1eef7-0b75-4e53-8241-cbf566a2b1e0"
      },
      "source": [
        "customers = pd.read_excel('Dataset/Dataset.xlsx',\n",
        "                          sheet_name=0,\n",
        "                          header=0,\n",
        "                          index_col=False,\n",
        "                          keep_default_na=True)"
      ],
      "id": "16f1eef7-0b75-4e53-8241-cbf566a2b1e0",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "32f6e9ea-c9cb-43ae-8a3c-a8de1f9bce95",
        "outputId": "8691f647-e650-4b00-d3d2-3c072f99ace3"
      },
      "source": [
        "customers.head()"
      ],
      "id": "32f6e9ea-c9cb-43ae-8a3c-a8de1f9bce95",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>WeekOfMonth</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>Make</th>\n",
              "      <th>AccidentArea</th>\n",
              "      <th>DayOfWeekClaimed</th>\n",
              "      <th>MonthClaimed</th>\n",
              "      <th>WeekOfMonthClaimed</th>\n",
              "      <th>Sex</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fault</th>\n",
              "      <th>PolicyType</th>\n",
              "      <th>VehicleCategory</th>\n",
              "      <th>VehiclePrice</th>\n",
              "      <th>FraudFound_P</th>\n",
              "      <th>PolicyNumber</th>\n",
              "      <th>RepNumber</th>\n",
              "      <th>Deductible</th>\n",
              "      <th>DriverRating</th>\n",
              "      <th>Days_Policy_Accident</th>\n",
              "      <th>Days_Policy_Claim</th>\n",
              "      <th>PastNumberOfClaims</th>\n",
              "      <th>AgeOfVehicle</th>\n",
              "      <th>AgeOfPolicyHolder</th>\n",
              "      <th>PoliceReportFiled</th>\n",
              "      <th>WitnessPresent</th>\n",
              "      <th>AgentType</th>\n",
              "      <th>NumberOfSuppliments</th>\n",
              "      <th>AddressChange_Claim</th>\n",
              "      <th>NumberOfCars</th>\n",
              "      <th>Year</th>\n",
              "      <th>BasePolicy</th>\n",
              "      <th>ClaimSize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dec</td>\n",
              "      <td>5</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Jan</td>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>Single</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sport - Liability</td>\n",
              "      <td>Sport</td>\n",
              "      <td>more than 69000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>300</td>\n",
              "      <td>1.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>none</td>\n",
              "      <td>3 years</td>\n",
              "      <td>26 to 30</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>none</td>\n",
              "      <td>1 year</td>\n",
              "      <td>3 to 4</td>\n",
              "      <td>1994</td>\n",
              "      <td>Liability</td>\n",
              "      <td>55526.069421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jan</td>\n",
              "      <td>3</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Jan</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>Single</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sport - Collision</td>\n",
              "      <td>Sport</td>\n",
              "      <td>more than 69000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>400</td>\n",
              "      <td>4.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>none</td>\n",
              "      <td>6 years</td>\n",
              "      <td>31 to 35</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>none</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Collision</td>\n",
              "      <td>59294.455571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Oct</td>\n",
              "      <td>5</td>\n",
              "      <td>Friday</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>Nov</td>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>47.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sport - Collision</td>\n",
              "      <td>Sport</td>\n",
              "      <td>more than 69000</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>400</td>\n",
              "      <td>3.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>1</td>\n",
              "      <td>7 years</td>\n",
              "      <td>41 to 50</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>none</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Collision</td>\n",
              "      <td>71756.941320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Jun</td>\n",
              "      <td>2</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Toyota</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Friday</td>\n",
              "      <td>Jul</td>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Third Party</td>\n",
              "      <td>Sedan - Liability</td>\n",
              "      <td>Sport</td>\n",
              "      <td>20000 to 29000</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>400</td>\n",
              "      <td>2.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>1</td>\n",
              "      <td>more than 7</td>\n",
              "      <td>51 to 65</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>more than 5</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Liability</td>\n",
              "      <td>7584.153493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Feb</td>\n",
              "      <td>1</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Feb</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>36.0</td>\n",
              "      <td>Third Party</td>\n",
              "      <td>Sport - Collision</td>\n",
              "      <td>Sport</td>\n",
              "      <td>more than 69000</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>400</td>\n",
              "      <td>1.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>1</td>\n",
              "      <td>7 years</td>\n",
              "      <td>36 to 40</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>1 to 2</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Collision</td>\n",
              "      <td>98545.374339</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Month  WeekOfMonth  DayOfWeek  ...  Year BasePolicy     ClaimSize\n",
              "0   Dec            5  Wednesday  ...  1994  Liability  55526.069421\n",
              "1   Jan            3  Wednesday  ...  1994  Collision  59294.455571\n",
              "2   Oct            5     Friday  ...  1994  Collision  71756.941320\n",
              "3   Jun            2   Saturday  ...  1994  Liability   7584.153493\n",
              "4   Feb            1   Saturday  ...  1994  Collision  98545.374339\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ImUvXZtj7iN7",
        "outputId": "5720a7cb-a75e-4fb5-df7a-747a5262c99e"
      },
      "source": [
        "customers.describe()"
      ],
      "id": "ImUvXZtj7iN7",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>WeekOfMonth</th>\n",
              "      <th>WeekOfMonthClaimed</th>\n",
              "      <th>Age</th>\n",
              "      <th>FraudFound_P</th>\n",
              "      <th>PolicyNumber</th>\n",
              "      <th>RepNumber</th>\n",
              "      <th>Deductible</th>\n",
              "      <th>DriverRating</th>\n",
              "      <th>Year</th>\n",
              "      <th>ClaimSize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11565.000000</td>\n",
              "      <td>11565.000000</td>\n",
              "      <td>11560.000000</td>\n",
              "      <td>11565.000000</td>\n",
              "      <td>11565.000000</td>\n",
              "      <td>11565.000000</td>\n",
              "      <td>11565.000000</td>\n",
              "      <td>11559.000000</td>\n",
              "      <td>11565.000000</td>\n",
              "      <td>11565.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.784003</td>\n",
              "      <td>2.701167</td>\n",
              "      <td>39.899567</td>\n",
              "      <td>0.059230</td>\n",
              "      <td>7710.474449</td>\n",
              "      <td>8.469780</td>\n",
              "      <td>407.617812</td>\n",
              "      <td>2.493468</td>\n",
              "      <td>1994.865975</td>\n",
              "      <td>22955.978035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.284854</td>\n",
              "      <td>1.258153</td>\n",
              "      <td>13.590556</td>\n",
              "      <td>0.236066</td>\n",
              "      <td>4453.762219</td>\n",
              "      <td>4.618952</td>\n",
              "      <td>43.397393</td>\n",
              "      <td>1.118102</td>\n",
              "      <td>0.801798</td>\n",
              "      <td>26988.811719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3851.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>4148.845001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7712.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1995.000000</td>\n",
              "      <td>8130.994563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11542.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1996.000000</td>\n",
              "      <td>46299.646944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15420.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1996.000000</td>\n",
              "      <td>141394.159289</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        WeekOfMonth  WeekOfMonthClaimed  ...          Year      ClaimSize\n",
              "count  11565.000000        11565.000000  ...  11565.000000   11565.000000\n",
              "mean       2.784003            2.701167  ...   1994.865975   22955.978035\n",
              "std        1.284854            1.258153  ...      0.801798   26988.811719\n",
              "min        1.000000            1.000000  ...   1994.000000       0.000000\n",
              "25%        2.000000            2.000000  ...   1994.000000    4148.845001\n",
              "50%        3.000000            3.000000  ...   1995.000000    8130.994563\n",
              "75%        4.000000            4.000000  ...   1996.000000   46299.646944\n",
              "max        5.000000            5.000000  ...   1996.000000  141394.159289\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2dD2IwTMtPZ",
        "outputId": "ce325a3f-cf51-4929-9f26-7179a6bbb358"
      },
      "source": [
        "customers['AddressChange_Claim'].value_counts()"
      ],
      "id": "J2dD2IwTMtPZ",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no change         10715\n",
              "4 to 8 years        496\n",
              "2 to 3 years        222\n",
              "1 year              128\n",
              "under 6 months        4\n",
              "Name: AddressChange_Claim, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HR5I-jcYMtPa",
        "outputId": "f4f8d34b-1a75-40c6-9bad-d7ece31d75fa"
      },
      "source": [
        "customers[customers['FraudFound_P']==1]['AddressChange_Claim'].value_counts()"
      ],
      "id": "HR5I-jcYMtPa",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no change         607\n",
              "2 to 3 years       40\n",
              "4 to 8 years       27\n",
              "1 year              8\n",
              "under 6 months      3\n",
              "Name: AddressChange_Claim, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0-aFHbyMtPa",
        "outputId": "09e19b5f-a2e8-43d8-da41-0afaaa430f52"
      },
      "source": [
        "customers['NumberOfSuppliments'].value_counts()"
      ],
      "id": "q0-aFHbyMtPa",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "none           5330\n",
              "more than 5    2875\n",
              "1 to 2         1846\n",
              "3 to 5         1514\n",
              "Name: NumberOfSuppliments, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbCsHNo0MtPa",
        "outputId": "3a831ae9-3d98-4a4a-8a32-f484b992ff54"
      },
      "source": [
        "customers[customers['FraudFound_P']==1]['NumberOfSuppliments'].value_counts()"
      ],
      "id": "mbCsHNo0MtPa",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "none           357\n",
              "more than 5    142\n",
              "1 to 2         113\n",
              "3 to 5          73\n",
              "Name: NumberOfSuppliments, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wV8zZWRIMtPa",
        "outputId": "d9c2553d-e0b3-410a-c483-37ad72357b4c"
      },
      "source": [
        "customers['AgentType'].value_counts()"
      ],
      "id": "wV8zZWRIMtPa",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "External    11394\n",
              "Internal      171\n",
              "Name: AgentType, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XAcfuGgMtPa",
        "outputId": "b874f6f1-72f6-4880-cdc5-1376802854a9"
      },
      "source": [
        "customers[customers['FraudFound_P']==1]['AgentType'].value_counts()"
      ],
      "id": "5XAcfuGgMtPa",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "External    681\n",
              "Internal      4\n",
              "Name: AgentType, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BhSPvbnMtPa",
        "outputId": "5737aa12-5ce1-4ee2-b8c4-33b9634d5668"
      },
      "source": [
        "customers['PoliceReportFiled'].value_counts()"
      ],
      "id": "9BhSPvbnMtPa",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     11235\n",
              "Yes      330\n",
              "Name: PoliceReportFiled, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKYL6YekMtPa",
        "outputId": "a8c845bc-0700-4f43-8f2f-13c71b5eb198"
      },
      "source": [
        "customers[customers['FraudFound_P']==1]['PoliceReportFiled'].value_counts()"
      ],
      "id": "wKYL6YekMtPa",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     672\n",
              "Yes     13\n",
              "Name: PoliceReportFiled, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqJ-ucXoMtPa",
        "outputId": "63858d16-0b1c-46ec-a0b7-321657f85a2a"
      },
      "source": [
        "customers['WitnessPresent'].value_counts()"
      ],
      "id": "yqJ-ucXoMtPa",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     11497\n",
              "Yes       68\n",
              "Name: WitnessPresent, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8fCZynOMtPa",
        "outputId": "48a4a9e6-ee9d-4616-fa80-5b9e6d12b617"
      },
      "source": [
        "customers[customers['FraudFound_P']==1]['WitnessPresent'].value_counts()"
      ],
      "id": "j8fCZynOMtPa",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     683\n",
              "Yes      2\n",
              "Name: WitnessPresent, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbFysLQJMtPa",
        "outputId": "9dcfd6ec-989d-40cc-fe3c-8bf5a1ce1c12"
      },
      "source": [
        "customers[(customers['WitnessPresent']=='Yes') & (customers['PoliceReportFiled']=='Yes')]['FraudFound_P']"
      ],
      "id": "UbFysLQJMtPa",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1182     0\n",
              "1504     0\n",
              "1802     0\n",
              "1933     0\n",
              "2064     0\n",
              "2074     0\n",
              "2347     0\n",
              "2881     0\n",
              "3002     0\n",
              "3794     0\n",
              "3888     0\n",
              "4693     0\n",
              "4897     0\n",
              "6403     0\n",
              "7037     0\n",
              "7113     0\n",
              "7241     0\n",
              "7320     0\n",
              "8285     0\n",
              "8346     0\n",
              "8824     0\n",
              "9339     0\n",
              "9478     0\n",
              "10330    0\n",
              "10568    0\n",
              "10604    0\n",
              "Name: FraudFound_P, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a22ktrDjPZIP"
      },
      "source": [
        "##Fraud commited with respect to Gender"
      ],
      "id": "a22ktrDjPZIP"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "b439c0fe-e27d-4f6d-90d4-a3b4114d8cac",
        "outputId": "2a5f1e3f-ed47-4f15-b81c-06fd83c7bd00"
      },
      "source": [
        "plt.subplots(figsize=(5, 6))\n",
        "sns.countplot(x='Sex',\n",
        "              data=customers[customers['FraudFound_P']==1],\n",
        "              palette='inferno',\n",
        "              order=customers['Sex'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud Done')\n",
        "plt.title('Gender vs Number of Fraud Done')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print('Percentage of Fraud Done from Female Insurance Policy Holder', format((73/1797)*100, '.2f'), '%')\n",
        "print('Percentage of Fraud Done from Male Insurance Policy Holder', format((612/9767)*100, '.2f'), '%')"
      ],
      "id": "b439c0fe-e27d-4f6d-90d4-a3b4114d8cac",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of Fraud Done from Female Insurance Policy Holder 4.06 %\n",
            "Percentage of Fraud Done from Male Insurance Policy Holder 6.27 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4566f19f"
      },
      "source": [
        "##Claim and Fraud with respect to the Age of the customer\n",
        "\n"
      ],
      "id": "4566f19f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "0254d9be-79c9-430d-8c11-ed56cc06b39a",
        "outputId": "587fe187-c520-43c8-d22f-c4238992171f"
      },
      "source": [
        "plt.subplots(figsize=(15, 6))\n",
        "sns.countplot(x='AgeOfPolicyHolder',\n",
        "              data=customers,\n",
        "              palette='RdYlGn',\n",
        "              order=customers['AgeOfPolicyHolder'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Claims')\n",
        "plt.xlabel('Age of Policy Holder')\n",
        "plt.title('Age of Policy Holder vs Number of Claims')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = customers['AgeOfPolicyHolder'].value_counts().index\n",
        "y = np.array(customers['AgeOfPolicyHolder'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum()) * 100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['Age Group', 'Claims', 'Percentage-%'])"
      ],
      "id": "0254d9be-79c9-430d-8c11-ed56cc06b39a",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JknrEJFCSJEmSesQkUJIkSZJ6ZGhJYJIHJDkzyY+TLE7yjrb8U0kuS3Je+9imLU+SDydZkuT8JE8aONdeSS5pH3sNK2ZJkiRJGnerDfHcdwA7VtUtSVYHvp/kG+22f6iqYyft/1xgy/axPfBxYPskDwUOBBYBBZyT5Piqun6IsUuSJEnSWBpaS2A1bmlXV28fNcMhuwGfbo87HVg3yUbAzsBJVXVdm/idBOwyrLglSZIkaZwNdUxgklWTnAdcS5PIndFuOrjt8vnBJGu2ZZsAVwwcfmVbNl25JEmSJGkFDTUJrKq7qmobYFNguyR/BhwAPAbYFngo8Na5eK4k+yQ5O8nZy5Ytm4tTSpIkSdLYGcnsoFV1A3AKsEtVXdV2+bwD+CSwXbvbUmCzgcM2bcumK5/8HIdV1aKqWrRgwYJhvAxJkiRJmveGOTvogiTrtstrAc8BftqO8yNJgN2Bn7SHHA+8pp0l9MnAjVV1FfBNYKck6yVZD9ipLZMkSZIkraBhzg66EXBkklVpks1jquprSb6TZAEQ4Dzgb9r9TwR2BZYAtwF7A1TVdUneBZzV7vfOqrpuiHFLkiRJ0tgaWhJYVecDT5yifMdp9i9g32m2HQEcMacBSpIkSVIPjWRMoCRJkiRp5WASKEmSJEk9YhIoSZIkST1iEihJkiRJPWISKEmSJEk9YhIoSZIkST1iEihJkiRJPWISKEmSJEk9YhIoSZIkST1iEihJkiRJPWISKEmSJEk9YhIoSZIkST1iEihJkiRJPWISKEmSJEk9YhIoSZIkST1iEihJkiRJPWISKEmSJEk9YhIoSZIkST1iEihJkiRJPWISKEmSJEk9YhIoSZIkST1iEihJkiRJPWISKEmSJEk9YhIoSZIkST1iEihJkiRJPWISKEmSJEk9YhIoSZIkST1iEihJkiRJPTK0JDDJA5KcmeTHSRYneUdb/ogkZyRZkuQLSdZoy9ds15e02xcOnOuAtvziJDsPK2ZJkiRJGnfDbAm8A9ixqp4AbAPskuTJwPuBD1bVFsD1wOvb/V8PXN+Wf7DdjyRbA3sAjwV2AT6WZNUhxi1JkiRJY2toSWA1bmlXV28fBewIHNuWHwns3i7v1q7Tbn9WkrTlR1fVHVV1GbAE2G5YcUuSJEnSOBvqmMAkqyY5D7gWOAn4OXBDVd3Z7nIlsEm7vAlwBUC7/UZg/cHyKY6RJEmSJK2A1YZ58qq6C9gmybrAccBjhvVcSfYB9gHYfPPNh/U0up8ue/srug5hXnjEez7fdQiSJEkaUyOZHbSqbgBOAZ4CrJtkIvncFFjaLi8FNgNot68D/GawfIpjBp/jsKpaVFWLFixYMJTXIUmSJEnz3TBnB13QtgCSZC3gOcBFNMngS9vd9gK+2i4f367Tbv9OVVVbvkc7e+gjgC2BM4cVtyRJkiSNs2F2B90IOLKdyXMV4Jiq+lqSC4Gjk7wb+BFweLv/4cBnkiwBrqOZEZSqWpzkGOBC4E5g37abqSRJkiRpBQ0tCayq84EnTlF+KVPM7llVtwMvm+ZcBwMHz3WMkiRJktQ3IxkTKEmSJElaOZgESpIkSVKPmARKkiRJUo+YBEqSJElSj5gESpIkSVKPmARKkiRJUo+YBEqSJElSj5gESpIkSVKPmARKkiRJUo+YBEqSJElSj5gESpIkSVKPLDcJTPJvSR6SZPUkJydZluRVowhOkiRJkjS3ZtMSuFNV3QQ8H/gFsAXwD8MMSpIkSZI0HLNJAldr/z4P+GJV3TjEeCRJkiRJQ7Ta8nfha0l+CvwWeFOSBcDtww1LkiRJkjQMy20JrKq3AU8FFlXV74Fbgd2GHZgkSZIkae4ttyUwyarADsDCJIP7f2BoUUmSJEmShmI23UFPoOn+eQFw93DDkSRJkiQN02ySwE2r6vFDj0SSJEmSNHSzmR30G0l2GnokkiRJkqShm01L4OnAcUlWAX4PBKiqeshQI5MkSZIkzbnZJIEfAJ4CXFBVNeR4JEmSJElDNJvuoFcAPzEBlCRJkqT5bzYtgZcCpyb5BnDHRGFVeYsISZIkSZpnZpMEXtY+1mgfkiRJkqR5arlJYFW9YxSBSJIkSZKGb9okMMmHqurNSU4A7jMesKpeONTIJEmSJElzbqaWwM+0f/9jFIFIkiRJkoZv2tlBq+qc9u9pUz2Wd+IkmyU5JcmFSRYn2b8tPyjJ0iTntY9dB445IMmSJBcn2XmgfJe2bEmSt92/lyxJkiRJ/bXcMYFJtgTeC2wNPGCivKoeuZxD7wT+vqrOTbI2cE6Sk9ptH6yqe7UwJtka2AN4LLAx8O0kW7WbPwo8B7gSOCvJ8VV14XJfnSRJkiTpXmZzn8BPAh+nSeqeCXwa+OzyDqqqq6rq3Hb5ZuAiYJMZDtkNOLqq7qiqy4AlwHbtY0lVXVpVvwOObveVJEmSJK2g2SSBa1XVyUCq6vKqOgh43oo8SZKFwBOBM9qi/ZKcn+SIJOu1ZZvQ3Jh+wpVt2XTlkiRJkqQVNJsk8I4kqwCXJNkvyYuAB8/2CZI8GPgS8OaquommVfFRwDbAVcAhKx72lM+zT5Kzk5y9bNmyuTilJEmSJI2d2SSB+wMPBP4O+HPg1cBeszl5ktVpEsDPVdWXAarqmqq6q6ruBv6HprsnwFJgs4HDN23Lpiu/l6o6rKoWVdWiBQsWzCY8SZIkSeqd2dws/qx28RZg79meOEmAw4GLquoDA+UbVdVV7eqLgJ+0y8cDn0/yAZqJYbYEzgQCbJnkETTJ3x7AK2YbhyRJkiTpHjPdLH7Km8RPmMXN4p9G02p4QZLz2rK3A3sm2aY99y+AN7bnW5zkGOBCmklo9q2qu9pY9gO+CawKHFFVi5f/0iRJkiRJk83UEni/bhJfVd+nacWb7MQZjjkYOHiK8hNnOk6SJEmSNDszJYEXAgsm34+vvZ+fM69IkiRJ0jw008QwHwE2mKJ8feDQ4YQjSZIkSRqmmZLALarqu5MLq+p7wOOHF5IkSZIkaVhmSgLXnmHb6nMdiCRJkiRp+GZKApck2XVyYZLnApcOLyRJkiRJ0rDMNDHMm4GvJ3k5cE5btgh4CvD8YQcmSZIkSZp707YEVtUlwOOA04CF7eM04PFV9bNRBCdJkiRJmlsztQRSVXcAnxxRLJIkSZKkIZtpTKAkSZIkacyYBEqSJElSj0ybBCY5uf37/tGFI0mSJEkappnGBG6U5KnAC5McDWRwY1WdO9TIJEmSJElzbqYk8F+BfwE2BT4waVsBOw4rKEmSJEnScEybBFbVscCxSf6lqt41wpgkSZIkSUMy4y0iAKrqXUleCDy9LTq1qr423LAkSZIkScOw3NlBk7wX2B+4sH3sn+Q9ww5MkiRJkjT3ltsSCDwP2Kaq7gZIciTwI+DtwwxMkiRJkjT3ZnufwHUHltcZRiCSJEmSpOGbTUvge4EfJTmF5jYRTwfeNtSoJEmSJElDMZuJYY5KciqwbVv01qq6eqhRSZIkSZKGYjYtgVTVVcDxQ45FkiRJkjRksx0TKEmSJEkaAyaBkiRJktQjM3YHTbIqsLiqHjOieCTNsWu//A9dhzAvPOzF/951CJIkSSMxY0tgVd0FXJxk8xHFI0mSJEkaotlMDLMesDjJmcCtE4VV9cKhRSVJkiRJGorZJIH/MvQoJEmSJEkjMZv7BJ6W5OHAllX17SQPBFYdfmiSJEmSpLm23NlBk7wBOBb477ZoE+ArszhusySnJLkwyeIk+7flD01yUpJL2r/rteVJ8uEkS5Kcn+RJA+faq93/kiR7/TEvVJIkSZI0u1tE7As8DbgJoKouAR42i+PuBP6+qrYGngzsm2Rr4G3AyVW1JXByuw7wXGDL9rEP8HFokkbgQGB7YDvgwInEUZIkSZK0YmaTBN5RVb+bWEmyGlDLO6iqrqqqc9vlm4GLaFoRdwOObHc7Eti9Xd4N+HQ1TgfWTbIRsDNwUlVdV1XXAycBu8zq1UmSJEmS7mU2SeBpSd4OrJXkOcAXgRNW5EmSLASeCJwBbFhVV7WbrgY2bJc3Aa4YOOzKtmy6ckmSJEnSCppNEvg2YBlwAfBG4ETgn2f7BEkeDHwJeHNV3TS4raqKWbQqzvJ59klydpKzly1bNhenlCRJkqSxM5vZQe9OciRNK14BF7fJ23IlWZ0mAfxcVX25Lb4myUZVdVXb3fPatnwpsNnA4Zu2ZUuBZ0wqP3WKOA8DDgNYtGjRnCSWkiRJkjRuZjM76POAnwMfBv4TWJLkubM4LsDhwEVV9YGBTccDEzN87gV8daD8Ne0soU8Gbmy7jX4T2CnJeu2EMDu1ZZIkSZKkFTSbm8UfAjyzqpYAJHkU8HXgG8s57mnAq4ELkpzXlr0deB9wTJLXA5cDL2+3nQjsCiwBbgP2Bqiq65K8Czir3e+dVXXdLOKWJEmSJE0ymyTw5okEsHUpcPPyDqqq7wOZZvOzpti/aG5HMdW5jgCOWH6okiRJkqSZTJsEJnlxu3h2khOBY2jGBL6Me1rlJEmSJEnzyEwtgS8YWL4G+Mt2eRmw1tAikiRJkiQNzbRJYFXtPcpAJEmSJEnDt9wxgUkeAfwtsHBw/6p64fDCkiRJkiQNw2wmhvkKza0eTgDuHm44kiRJkqRhmk0SeHtVfXjokUiSJEmShm42SeChSQ4EvgXcMVFYVecOLSpJkiRJ0lDMJgl8HM1N33fknu6g1a5LkiRJkuaR2SSBLwMeWVW/G3YwkiRJkqThWmUW+/wEWHfYgUiSJEmShm82LYHrAj9Nchb3HhPoLSIkSZIkaZ6ZTRJ44NCjkCRJkiSNxHKTwKo6bRSBSJIkSZKGb7lJYJKbaWYDBVgDWB24taoeMszAJEmSJElzbzYtgWtPLCcJsBvw5GEGJUmSJEkajtnMDvoH1fgKsPOQ4pEkSZIkDdFsuoO+eGB1FWARcPvQIpIkSZIkDc1sZgd9wcDyncAvaLqESpIkSZLmmdmMCdx7FIFIkiRJkoZv2iQwyb/OcFxV1buGEI8kSZIkaYhmagm8dYqyBwGvB9YHTAIlSZIkaZ6ZNgmsqkMmlpOsDewP7A0cDRwy3XGSJEmSpJXXjGMCkzwUeAvwSuBI4ElVdf0oApMkSZIkzb2ZxgT+O/Bi4DDgcVV1y8iikiRJkiQNxUw3i/97YGPgn4FfJbmpfdyc5KbRhCdJkiRJmkszjQmcKUGUJEmSJM1DJnqSJEmS1CMmgZIkSZLUI0NLApMckeTaJD8ZKDsoydIk57WPXQe2HZBkSZKLk+w8UL5LW7YkyduGFa8kSZIk9cEwWwI/BewyRfkHq2qb9nEiQJKtgT2Ax7bHfCzJqklWBT4KPBfYGtiz3VeSJEmS9EeY8T6B90dVfTfJwlnuvhtwdFXdAVyWZAmwXbttSVVdCpDk6HbfC+c4XEmSJEnqhS7GBO6X5Py2u+h6bdkmwBUD+1zZlk1XLkmSJEn6I4w6Cfw48ChgG+Aq4JC5OnGSfZKcneTsZcuWzdVpJUmSJGmsjDQJrKprququqrob+B/u6fK5FNhsYNdN27Lpyqc692FVtaiqFi1YsGDug5ckSZKkMTDSJDDJRgOrLwImZg49HtgjyZpJHgFsCZwJnAVsmeQRSdagmTzm+FHGLEmSJEnjZGgTwyQ5CngGsEGSK4EDgWck2QYo4BfAGwGqanGSY2gmfLkT2Leq7mrPsx/wTWBV4IiqWjysmCVJkiRp3A1zdtA9pyg+fIb9DwYOnqL8RODEOQxNkiRJknqri9lBJUmSJEkdMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHlmt6wAkadzc+LPDug5hXlhnq326DkGSpF4aWktgkiOSXJvkJwNlD01yUpJL2r/rteVJ8uEkS5Kcn+RJAyxZphoAABkiSURBVMfs1e5/SZK9hhWvJEmSJPXBMLuDfgrYZVLZ24CTq2pL4OR2HeC5wJbtYx/g49AkjcCBwPbAdsCBE4mjJEmSJGnFDS0JrKrvAtdNKt4NOLJdPhLYfaD809U4HVg3yUbAzsBJVXVdVV0PnMR9E0tJkiRJ0iyNemKYDavqqnb5amDDdnkT4IqB/a5sy6YrlyRJkiT9ETqbHbSqCqi5Ol+SfZKcneTsZcuWzdVpJUmSJGmsjDoJvKbt5kn799q2fCmw2cB+m7Zl05XfR1UdVlWLqmrRggUL5jxwSZIkSRoHo04CjwcmZvjcC/jqQPlr2llCnwzc2HYb/SawU5L12glhdmrLJEmSJEl/hKHdJzDJUcAzgA2SXEkzy+f7gGOSvB64HHh5u/uJwK7AEuA2YG+AqrouybuAs9r93llVkyebkSRJkiTN0tCSwKrac5pNz5pi3wL2neY8RwBHzGFokiRJktRbnU0MI0mSJEkaPZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqEZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqEZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqEZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqEZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqEZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqEZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqEZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqEZNASZIkSeoRk0BJkiRJ6hGTQEmSJEnqkU6SwCS/SHJBkvOSnN2WPTTJSUkuaf+u15YnyYeTLElyfpIndRGzJEmSJI2DLlsCn1lV21TVonb9bcDJVbUlcHK7DvBcYMv2sQ/w8ZFHKkmSJEljYmXqDrobcGS7fCSw+0D5p6txOrBuko26CFCSJEmS5ruuksACvpXknCT7tGUbVtVV7fLVwIbt8ibAFQPHXtmWSZIkSZJW0GodPe8OVbU0ycOAk5L8dHBjVVWSWpETtsnkPgCbb7753EUqSZIkSWOkk5bAqlra/r0WOA7YDrhmoptn+/fadvelwGYDh2/alk0+52FVtaiqFi1YsGCY4UuSJEnSvDXyJDDJg5KsPbEM7AT8BDge2KvdbS/gq+3y8cBr2llCnwzcONBtVJIkSZK0ArroDrohcFySief/fFX9b5KzgGOSvB64HHh5u/+JwK7AEuA2YO/RhyxJkiRJ42HkSWBVXQo8YYry3wDPmqK8gH1HEJokaZ767e0/7DqEeWGtBzyl6xAkSSuBlekWEZIkSZKkITMJlCRJkqQeMQmUJEmSpB7p6j6BkiRpHrvgyv/uOoR54XGbvrHrECTpPmwJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeWa3rACRJkrR8nzv7gK5DmBdeuei9XYcgrfRsCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB4xCZQkSZKkHjEJlCRJkqQeMQmUJEmSpB5ZresAJEmSpJXRW07Yv+sQ5oUPvODQrkPQCpo3LYFJdklycZIlSd7WdTySJEmSNB/NiyQwyarAR4HnAlsDeybZutuoJEmSJGn+mRdJILAdsKSqLq2q3wFHA7t1HJMkSZIkzTvzZUzgJsAVA+tXAtt3FIskSZKkIdj6fS/pOoR54cK3fel+HZ+qmqNQhifJS4Fdquqv2/VXA9tX1X4D++wD7NOuPhq4eOSBzo0NgF93HUTPWOejZ52PnnU+etb56Fnno2edj551Pnrztc4fXlULptowX1oClwKbDaxv2pb9QVUdBhw2yqCGIcnZVbWo6zj6xDofPet89Kzz0bPOR886Hz3rfPSs89EbxzqfL2MCzwK2TPKIJGsAewDHdxyTJEmSJM0786IlsKruTLIf8E1gVeCIqlrccViSJEmSNO/MiyQQoKpOBE7sOo4RmPddWuch63z0rPPRs85HzzofPet89Kzz0bPOR2/s6nxeTAwjSZIkSZob82VMoCRJkiRpDpgESpIkSVKPmARKkqR5L8lDu45BkuYLxwSuJJLsAGwH/KSqvtV1POMqyWOA3YBN2qKlwPFVdVF3UY23JDsDu3PvOv9qVf1vd1GNrySPr6rz2+XVgbfSfrYA766q27qMb1x5nY9Wkn+uqne3y1sDXwFWBwL8VVWd0WV84yjJl4EvA1+pqlu6jqcP/DzvRl8+z00CO5LkzKrarl1+A7AvcBywE3BCVb2vy/jGUZK3AnsCRwNXtsWb0tx38mjrfO4l+RCwFfBp7l3nrwEuqar9u4ptXCU5t6qe1C4fAqwPfJLmP7T1q+o1XcY3jrzOR2/Sdf514D+r6htJtgM+VFVP7TbC8ZNkKfBDYEfg28BRwNer6nedBjbG/DwfvT59npsEdiTJj6rqie3yWcCuVbUsyYOA06vqcd1GOH6S/Ax4bFX9flL5GsDiqtqym8jGV5KfVdVWU5QH+Jl1PvcmfbacB2xbVb9v6/zHVfX4biMcP17nozfpy/Efrvmp1jU3Juo1yUNoetTsCWwLfA04yl5Mc8/P89Hr0+e5YwK7s0qS9ZKsT5OMLwOoqluBO7sNbWzdDWw8RflG7TbNvduTbDtF+bbA7aMOpifWSfKiJC8B1pz40aOaX/z81W84vM5H75FJjk9yArBpkgcObFu9q6DGXAFU1U1V9Zmq2hV4DHAG8LZOIxtffp6PXm8+z+fNzeLH0DrAOTTjFyrJRlV1VZIHt2Wae28GTk5yCXBFW7Y5sAWwX2dRjbfXAh9Psjb3dKvYDLix3aa5dxrwwnb59CQbVtU1Sf4E+HWHcY2z1+J1Pmq7TVpfBSDJhsDHRx9OL9xnHGBV/Qb4r/ahuefn+ei9lp58ntsddCXT/pq5YVVd1nUs4yjJKjSDqgcH+55VVXd1F9X4a//D+kOdV9XVXcYjDYPXuSSNhz58ntsdtENJVmmTEpKskeRJwANMAIenqu6uqtOr6ktV9SXgSSaAw1dVV1fVOcDFwMZJ1u06pnGVZPMkD2iXk2TvJB9J8qYk9v4YkvYLA+11/kvgqe2slRqCJPsl2aBd3iLJd5PckOSMJH/WdXx9k+Q5XccwrpI8JMmjpih3POBw/aaqzmkfVwNMfOaMC5PAjiTZHbgKWJpkN+B7wL8D5yd5QafBjakkb5n8AN45sKw5luRjA8s7ABcChwAXJNm1s8DG24nc89n+PuB5NGN2tgUO6yqocZbkjTSzJp6e5E00E2U8Dzguyes7DW58vamqJrrDHQp8sKrWpZlC/7+7C6u3Du86gHGU5OXAT4EvJVk8aazap7qJarwleWaSK4GrknwrycKBzWM1+ZG/CnfnQOAJwFrAj2lmfLo4ycOBLwEndBncmHoHzRfkxdwz7nJVYO3OIhp/Tx5Yfhewe1Wdm+SRwDE074fm1ioD9456Ns1ny93AZ5P8uMO4xtl+wGNpPs8vB7aoqquTrAecgl+Qh2Hw+8vDquo4gKo6tR3LozmW5PjpNtHcukBz7+3An7dzRmwHfCbJAe317vwRw/FvwM5VtTjJS4GTkry6qk5nzOrcJLBDA83Lv6yqi9uyyye6iGrOPZamFepBwDuq6rYke1XVOzqOqy8eUlXnAlTVpV7nQ3NFkh2r6jvAL2gGtF/ezkSs4fh9m3jfluTnE5/tVXV9EgfeD8exST4FvJOmxfXNNPfa3ZGmO67m3l8Ar+K+E8SEZqy95t6qVXUVQFWdmeSZwNeSbIazgw7LGlW1GKCqjk1yEfDlNPeaHqs6NwnsUJJV2l/oXzdQtiqwRndRja+q+iXwsrb77UlJPth1TD3wmCTn03xJWJhkvfaL8Sp4nQ/LXwOfTnIQzWxm57X3l1oXsNvzcFSS1dvp2583UdiOzfTHjiGoqn9K8lqaG5Y/ClgT2Af4CvDKDkMbZ6cDt1XVaZM3JLm4g3j64OYkj6qqnwO0LYLPoLnOH9tpZOPr90n+ZODHvMVJnkXTzf8+YzPnM2cH7Ujbr/uCqrp9UvlCYIeq+mwXcfVFkgcBBwHbV9XTOw5nbLXdmwf9qr3R7QbA06vqy13E1QdJ/hTYiubHvitpZsH1fphDkGRzmmv7zknlmwB/WlXf7iYySfNZkicAt1bVkknlqwMvr6rPdRPZ+ErybGBZVf14Uvk6wH5VdXA3kc09k0BJkiRJ6hG7qUiSJElSj5gESpKkeae9D+ZmXcchSfOR3UFXEkkeDFBVk2fd0pBY56NnnY9WkjVoxgUCXNxOXKIhss5HK8kFVfW4ruPoG6/z0bPOuzHO31tsCexYkscl+RHNvesuTHJOkj/rOq5xZp2PnnU+eu0McpcAHwU+BvwsiZMgDZF13olzJ91AW0PmdT561vno9eF7iy2BHUvyA+CfquqUdv0ZwHuq6qmdBjbGrPPRs85HL8k5wCsm7kGaZCvgqKr6824jG1/W+egl+SmwBXA5cCvN7Wiqqh7faWBjzOt89Kzz0evD9xbvE9i9B01cYABVdWp7+wINj3U+etb56K0+8YUBoKp+1k4rruGxzkdv564D6CGv89Gzzkdv7L+3mAR279Ik/wJ8pl1/FXBph/H0gXU+etb56J2d5BPAxD1HXwmc3WE8fWCdj1hVXZ5kB2DLqvpkkgXAg7uOa8x5nY+edT56Y/+9xe6gHUuyHvAOYAeggO8BB1XVDZ0GNsas89GzzkcvyZrAvjR1Dk2df7SqftddVOPNOh+9JAcCi4BHV9VWSTYGvlhVT+s4tLHldT561vno9eF7i0lgx5K8rKq+uLwyzR3rfPSs89FLsn9VHbq8Ms0d63z0kpwHPBE4t6qe2Jad75jA4fE6Hz3rfPT68L3F2UG7d8AsyzR3rPPRs85Hb68pyl476iB6xjofvd9V82t2AYzbmJ2VlNf56Fnnozf231scE9iRJM8FdgU2SfLhgU0PAe7sJqrxZp2PnnU+ekn2BF4BPCLJ8QOb1gau6yaq8Wadd+qYJP8NrJvkDcDrgP/pOKax5HU+etb56PXpe4tJYHd+RTOo94XAOQPlNwP/t5OIxp91PnrW+ej9ALgK2AA4ZKD8ZuD8TiIaf9Z5R6rqP5I8B7gJeDTwr1V1UsdhjSuv89GzzkevN99bHBPYsSSrV9Xvu46jT6zz0bPOJQ1DkrcAX6iqpV3HIml89OF7i2MCOzbuF9jKyDofPetc0pCsDXwryfeS7Jdkw64DkjT/9eF7iy2BkiRpXkvyeOCvgJcAV1bVszsOSZJWarYESpI0x5Ks33UMPXMtcDXwG+BhHcfSG17n0vxlErgSSnJY1zGMo7ar0Abt8hZJvpvkhiRnJHlc1/H1jdf56CX5RtcxjKMk7xv4bFmU5FLgjCSXJ/nLjsMba0n+T5JTgZOB9YE3eI/A4fA6X7n4eT4c7bV9SpLPJtksyUlJbkxyVpIndh3fXLI7aEeSPHS6TcCPq2rTUcbTB0kWV9Vj2+WvA5+oquOSPAM4uKqe1mmAY8jrfPSSPGm6TcDXqmqjUcbTB0kuqKrHtcunAP9YVWcl2Qr4fFUt6jbC8ZXkvTQTw5zXdSzjzut89Pw8H70kZwIHAusC/wb836o6NsmzgHdX1VM6DXAOeYuI7iwDLqf5hzyh2nW7sgzH4PX+sKo6DqCqTk2ydkcxjTuv89E7CziNe9f5hHVHHEtfrJZktaq6E1irqs4CqKqfJVmz49jGWlUdkOQJSfZri75XVT/uNKjx5XU+en6ej97qVfUNgCTvr6pjAarq5CT/0W1oc8sksDuXAs+qql9O3pDkig7i6YNjk3wKeCdwXJI3A8cBOwL3eR80J7zOR+8i4I1VdcnkDdb50HwMODHJ+4D/TXIo8GWazxZbqIYoyd8B+9DUN8BnkxxWVR/pMKxx5XU+en6ej97tSXYC1gEqye5V9ZW2y/NdHcc2p+wO2pEk+wLfn+oXyyR/639gw5HktcCbgEcBawJXAF8B3l9VN3YY2ljyOh+9JC8FLqiqi6fYtntVfaWDsMZe2638TcBWND+wTny2fLIPU413Jcn5wFOq6tZ2/UHADx0XOBxe56Pl5/noJXkCTTfQu2luDv8mYC9gKbBPVf1/HYY3p0wCJUnSvJTkAmDbqrq9XX8AcNbE2DVJ0tScHbRDSbZLsm27vHWStyTZteu4xpl13r0kn+46hj5JskN7ne/UdSzjLMljkjwryYMnle/SVUw98UmaGSoPSnIQcDpweLch9U+SvbuOYVz52bLyGLfr3JbAjiQ5EHguTXeKk4DtgVOA5wDfrKqDOwxvLFnno5fk+MlFwDOB7wBU1QtHHtSYS3JmVW3XLr8B2Jdm7OtOwAlV9b4u4xtH7bi0fWnG72wD7F9VX223nVtV083wpznQzqC4Q7v6var6UZfx9FGSX1bV5l3HMW78bFm5jNt1bhLYkbYLyzY049KuBjatqpuSrAWc4XiGuWedj16Sc4ELgU9wz6ygRwF7AFTVad1FN56S/KiqntgunwXsWlXL2rFSp9tNbu61ny1PqapbkiwEjgU+U1WHDr4f0nzWjr+cchOwVVU5Q+gc87Nl9Pp0nTs7aHfurKq7gNuS/LyqbgKoqt8mubvj2MaVdT56i4D9gX8C/qGqzkvyW5O/oVolyXo03f1TVcsAqurWJHd2G9rYWqWqbgGoql+0k2ccm+ThTD21uzQfbQjsDFw/qTzAD0YfTi/42TJ6vbnOTQK787skD6yq24A/nyhMsg7NjESae9b5iFXV3cAHk3yx/XsNfu4M2zrAOTT/YVWSjarqqnY8iV8ahuOaJNtM3LC8/dX++cARgC2vGhdfAx48cZ0PSnLq6MPpBT9bRq8317ndQTuSZM2qumOK8g2Ajarqgg7CGmvWefeSPA94WlW9vetY+ibJA4ENq+qyrmMZN0k2pelpcPUU2542TlOKSxodP1s0TCaBkiRJktQj3iJC0v/f3t2FWFWFYRz/P5lBYJGKRZg1QaJkoxNTweQHWuJVYqRg4xCl1ZWUN9lFVCQZfXhhaRmBIGqmWWIXEtngmNnkR9Z8FtpNCUEEhRgTQVBvF3sd5zhsjzPTjHNO5/nBMPustde7330uhnnZa+1lZmZmZlXERaCZmZmZmVkVcRFoZmZlSdL9kkLS1EtwrQmSjklqkzS7T99nkk5J6pDUKmlKiTg1krrT8R2SNgxBbj+mtcuFz3Ml7bvImEckvXmBvp7/mpOZmVU2F4FmZlauGoEv0u/hdi/QFRG3R8ThnP6miJgBbAXW9SdgRJyIiCeHMslLTRn/r2Bm9j/jP+xmZlZ20pYWs4BHgQeL2i+TtEnSSUnNkj6WtCT11Us6JOlrSfslXZ8Tt0ZSi6ROSQck3SipDngNWCSpXdKVJVL7HLglFUfrJHVL6pK0NOda557YSRojaUs6t1PSYkkrJL1edP7jktYP8HsaJ+mjFPOopOk559ws6Ui69to+faslfZXGryn6jk5J2gZ0A5MGkpOZmZU/F4FmZlaOFgGfRMT3wG+SCnt7PgDUALcCDwENAJJGAxuBJRFRT7aP1ks5cTcCWyNiOrAD2JD2g3oeeD8i6iLizxJ5LQS6Uh51wAxgPrAur+gs8hxwNiJq07VbgN3AwpQ7wPKUd56DqUBtBzYXta8B2lLMZ4BtOWPfAN6OiFrg50KjpAXAZOCudC/1kuak7snApoiYFhGnS9yXmZlVIBeBZmZWjhqBXel4F71TQmcBH0TEP2nvrIOpfQpwG9CcCqVngRty4jYA76Xj7Slef+xIcWcCT6VxOyPi74j4BTgE3Fli/HzgrcKHiDgTET1kxeB9ad3j6BL7lc5LBWod8FhR+6x0H0RECzBe0tV9xs4Edqbj7UXtC9JPG/ANMJWs+AM4HRFHS9yPmZlVsMtHOgEzM7NiksYB9wC1kgIYBYSk1aWGAd9GRMMwpdUUESeKchyquJvJnuCdBLYMVdAceZsCC3g5It45r1GqAf4YxlzMzGyE+UmgmZmVmyXA9oi4KSJqImIS8AMwG2gFFqe1gdcBc9OYU8AESeemh0qalhP7S3rXGDYBeS+B6Y/DwFJJoyRNAOYAx0uc3wysLHyQNBYgIo6RrblbRu/TuoHm0ZRizgV+jYjf+5zTyvn3XLAfWJHWXyJpoqRrB5GDmZlVGBeBZmZWbhqBvX3a9qT2PcBPwHfAu2TTGM9GxF9kxeOrkjqAduDunNhPAMsldZKtKVw1yBz3Ap1AB9mUzqfT9NQLWQuMTS+S6QDmFfXtBloj4swg8niBbC1fJ/AK8HDOOauAlZK6gImFxoj4lGxq7JHU9yFw1SByMDOzCqOIvBkiZmZm5UnSmIjokTSe7OnbzIsUYGUtvUF0fUQcGOlczMysOnhNoJmZVZp9kq4BrgBerNQCMN3DcaDDBaCZmV1KfhJoZmZmZmZWRbwm0MzMzMzMrIq4CDQzMzMzM6siLgLNzMzMzMyqiItAMzMzMzOzKuIi0MzMzMzMrIq4CDQzMzMzM6si/wKNApklNp88CQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age Group</th>\n",
              "      <th>Claims</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31 to 35</td>\n",
              "      <td>4199</td>\n",
              "      <td>36.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36 to 40</td>\n",
              "      <td>3008</td>\n",
              "      <td>26.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41 to 50</td>\n",
              "      <td>2122</td>\n",
              "      <td>18.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51 to 65</td>\n",
              "      <td>1070</td>\n",
              "      <td>9.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>26 to 30</td>\n",
              "      <td>445</td>\n",
              "      <td>3.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>over 65</td>\n",
              "      <td>388</td>\n",
              "      <td>3.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>16 to 17</td>\n",
              "      <td>247</td>\n",
              "      <td>2.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>21 to 25</td>\n",
              "      <td>73</td>\n",
              "      <td>0.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>18 to 20</td>\n",
              "      <td>13</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Age Group  Claims Percentage-%\n",
              "0  31 to 35    4199        36.31\n",
              "1  36 to 40    3008        26.01\n",
              "2  41 to 50    2122        18.35\n",
              "3  51 to 65    1070         9.25\n",
              "4  26 to 30     445         3.85\n",
              "5   over 65     388         3.35\n",
              "6  16 to 17     247         2.14\n",
              "7  21 to 25      73         0.63\n",
              "8  18 to 20      13         0.11"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "9c98ca97-6853-47c0-af12-707dd429ddfc",
        "outputId": "d67c3777-40a5-4198-a88d-2ba246ce62a4"
      },
      "source": [
        "plt.subplots(figsize=(15,6))\n",
        "sns.countplot(x='AgeOfPolicyHolder',\n",
        "              data=customers[customers['FraudFound_P']==1],\n",
        "              palette='RdYlGn',\n",
        "              order=customers['AgeOfPolicyHolder'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Age of Policy Holder')\n",
        "plt.title('Age of Policy Holder vs Number of Fraud Done')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = customers[customers['FraudFound_P']==1]['AgeOfPolicyHolder'].value_counts().index\n",
        "y = np.array(customers[customers['FraudFound_P']==1]['AgeOfPolicyHolder'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum()) * 100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array,columns = ['Age Group', 'Claims', 'Percentage-%'])    "
      ],
      "id": "9c98ca97-6853-47c0-af12-707dd429ddfc",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age Group</th>\n",
              "      <th>Claims</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31 to 35</td>\n",
              "      <td>270</td>\n",
              "      <td>39.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36 to 40</td>\n",
              "      <td>174</td>\n",
              "      <td>25.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41 to 50</td>\n",
              "      <td>112</td>\n",
              "      <td>16.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51 to 65</td>\n",
              "      <td>53</td>\n",
              "      <td>7.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16 to 17</td>\n",
              "      <td>26</td>\n",
              "      <td>3.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>26 to 30</td>\n",
              "      <td>20</td>\n",
              "      <td>2.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>over 65</td>\n",
              "      <td>20</td>\n",
              "      <td>2.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>21 to 25</td>\n",
              "      <td>9</td>\n",
              "      <td>1.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>18 to 20</td>\n",
              "      <td>1</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Age Group  Claims Percentage-%\n",
              "0  31 to 35     270        39.42\n",
              "1  36 to 40     174        25.40\n",
              "2  41 to 50     112        16.35\n",
              "3  51 to 65      53         7.74\n",
              "4  16 to 17      26         3.80\n",
              "5  26 to 30      20         2.92\n",
              "6   over 65      20         2.92\n",
              "7  21 to 25       9         1.31\n",
              "8  18 to 20       1         0.15"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5f87839"
      },
      "source": [
        "###From the above two graph we get that policy holders who have age greater than 50 year have low chance of doing fraud"
      ],
      "id": "c5f87839"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wE7sJmQSOrQ6"
      },
      "source": [
        "##Age of Policy Holder and their Relationship Status vs Fraud"
      ],
      "id": "wE7sJmQSOrQ6"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "4ecc5a22-b8ab-480a-b017-4bc65ed5919b",
        "outputId": "3ce6e015-4d69-404a-f441-b1d5ae704c8a"
      },
      "source": [
        "plt.subplots(figsize=(15, 6))\n",
        "single = customers[customers['MaritalStatus']=='Single']\n",
        "sns.countplot(x='AgeOfPolicyHolder',\n",
        "              data=single[single['FraudFound_P']==1],\n",
        "              palette='RdYlGn',\n",
        "              order=single['AgeOfPolicyHolder'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Age of Policy Holder')\n",
        "plt.title('Age of Policy Holder - Relationship[Single] vs Number of Fraud Done')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = single[single['FraudFound_P']==1]['AgeOfPolicyHolder'].value_counts().index\n",
        "y = np.array(single[single['FraudFound_P']==1]['AgeOfPolicyHolder'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum())*100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['Age Group', 'Claims', 'Percentage-%'])   "
      ],
      "id": "4ecc5a22-b8ab-480a-b017-4bc65ed5919b",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age Group</th>\n",
              "      <th>Claims</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31 to 35</td>\n",
              "      <td>104</td>\n",
              "      <td>52.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16 to 17</td>\n",
              "      <td>26</td>\n",
              "      <td>13.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36 to 40</td>\n",
              "      <td>24</td>\n",
              "      <td>12.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26 to 30</td>\n",
              "      <td>17</td>\n",
              "      <td>8.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41 to 50</td>\n",
              "      <td>9</td>\n",
              "      <td>4.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>21 to 25</td>\n",
              "      <td>8</td>\n",
              "      <td>4.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>51 to 65</td>\n",
              "      <td>5</td>\n",
              "      <td>2.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>over 65</td>\n",
              "      <td>4</td>\n",
              "      <td>2.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>18 to 20</td>\n",
              "      <td>1</td>\n",
              "      <td>0.51</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Age Group  Claims Percentage-%\n",
              "0  31 to 35     104        52.53\n",
              "1  16 to 17      26        13.13\n",
              "2  36 to 40      24        12.12\n",
              "3  26 to 30      17         8.59\n",
              "4  41 to 50       9         4.55\n",
              "5  21 to 25       8         4.04\n",
              "6  51 to 65       5         2.53\n",
              "7   over 65       4         2.02\n",
              "8  18 to 20       1         0.51"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3461f073"
      },
      "source": [
        "###From this graph we can observe that single people having age between 31 to 35 have high chance of doing fraud"
      ],
      "id": "3461f073"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPpg4OZEQN9t"
      },
      "source": [
        "##Claim and Fraud with respect to the Vehicle category\n"
      ],
      "id": "PPpg4OZEQN9t"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "7a608679-9b56-4e25-a551-6831508495b0",
        "outputId": "5ec0171c-0300-4dbc-db93-e96138373706"
      },
      "source": [
        "plt.subplots(figsize=(8, 6))\n",
        "sns.countplot(x='VehicleCategory',\n",
        "              data=customers,\n",
        "              palette='RdYlGn',\n",
        "              order=customers['VehicleCategory'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Claims')\n",
        "plt.xlabel('Vehicle Category')\n",
        "plt.title('Vehicle Category vs Number of Claims')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = customers['VehicleCategory'].value_counts().index\n",
        "y = np.array(customers['VehicleCategory'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum()) * 100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i],'.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['Vehicle Category', 'Claims', 'Percentage-%'])    "
      ],
      "id": "7a608679-9b56-4e25-a551-6831508495b0",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Vehicle Category</th>\n",
              "      <th>Claims</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sedan</td>\n",
              "      <td>7253</td>\n",
              "      <td>62.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sport</td>\n",
              "      <td>4024</td>\n",
              "      <td>34.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Utility</td>\n",
              "      <td>288</td>\n",
              "      <td>2.49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Vehicle Category  Claims Percentage-%\n",
              "0            Sedan    7253        62.72\n",
              "1            Sport    4024        34.79\n",
              "2          Utility     288         2.49"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "50d4be2f-608d-4621-9344-6fa558ec26b4",
        "outputId": "af34ef24-162c-422a-d35f-5a1f1c7c7b71"
      },
      "source": [
        "plt.subplots(figsize=(8, 6))\n",
        "sns.countplot(x='VehicleCategory',\n",
        "              data=customers[customers['FraudFound_P']==1],\n",
        "              palette='RdYlGn',\n",
        "              order=customers['VehicleCategory'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Vehicle Category')\n",
        "plt.title('Vehicle Category vs Number of Fraud')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = customers[customers['FraudFound_P']==1]['VehicleCategory'].value_counts().index\n",
        "y = np.array(customers[customers['FraudFound_P']==1]['VehicleCategory'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum()) * 100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['Vehicle Category', 'Fraud', 'Percentage-%'])   "
      ],
      "id": "50d4be2f-608d-4621-9344-6fa558ec26b4",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Vehicle Category</th>\n",
              "      <th>Fraud</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sedan</td>\n",
              "      <td>588</td>\n",
              "      <td>85.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sport</td>\n",
              "      <td>61</td>\n",
              "      <td>8.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Utility</td>\n",
              "      <td>36</td>\n",
              "      <td>5.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Vehicle Category  Fraud Percentage-%\n",
              "0            Sedan    588        85.84\n",
              "1            Sport     61         8.91\n",
              "2          Utility     36         5.26"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjT9Gugf0anR"
      },
      "source": [
        "##Claim and Fraud with respect to the Policy type"
      ],
      "id": "TjT9Gugf0anR"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2d010807-caaf-4d7e-93d6-fb51dfeff8af",
        "outputId": "f381aa23-22f6-4ab7-fb09-6aa59b3baa32"
      },
      "source": [
        "customers.columns"
      ],
      "id": "2d010807-caaf-4d7e-93d6-fb51dfeff8af",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Month', 'WeekOfMonth', 'DayOfWeek', 'Make', 'AccidentArea',\n",
              "       'DayOfWeekClaimed', 'MonthClaimed', 'WeekOfMonthClaimed', 'Sex',\n",
              "       'MaritalStatus', 'Age', 'Fault', 'PolicyType', 'VehicleCategory',\n",
              "       'VehiclePrice', 'FraudFound_P', 'PolicyNumber', 'RepNumber',\n",
              "       'Deductible', 'DriverRating', 'Days_Policy_Accident',\n",
              "       'Days_Policy_Claim', 'PastNumberOfClaims', 'AgeOfVehicle',\n",
              "       'AgeOfPolicyHolder', 'PoliceReportFiled', 'WitnessPresent', 'AgentType',\n",
              "       'NumberOfSuppliments', 'AddressChange_Claim', 'NumberOfCars', 'Year',\n",
              "       'BasePolicy', 'ClaimSize'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1e4565a",
        "outputId": "73d097c4-17a4-485d-f9ed-9ec5e8e80bee"
      },
      "source": [
        "customers['PolicyType'].value_counts()"
      ],
      "id": "f1e4565a",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sedan - Collision       4186\n",
              "Sedan - Liability       3742\n",
              "Sedan - All Perils      3067\n",
              "Sport - Collision        262\n",
              "Utility - All Perils     251\n",
              "Utility - Collision       24\n",
              "Sport - All Perils        19\n",
              "Utility - Liability       13\n",
              "Sport - Liability          1\n",
              "Name: PolicyType, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "51651a18",
        "outputId": "10bb033b-c416-4cb3-ed16-80eec2a3d917"
      },
      "source": [
        "plt.subplots(figsize=(15, 6))\n",
        "sns.countplot(x='PolicyType',\n",
        "              data=customers,\n",
        "              palette='RdYlGn',\n",
        "              order=customers['PolicyType'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Claims')\n",
        "plt.xlabel('Policy Type')\n",
        "plt.title('Policy Type vs Number of Claims')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = customers['PolicyType'].value_counts().index\n",
        "y = np.array(customers['PolicyType'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum()) * 100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['PolicyType', 'Claims', 'Percentage-%'])   "
      ],
      "id": "51651a18",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PolicyType</th>\n",
              "      <th>Claims</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sedan - Collision</td>\n",
              "      <td>4186</td>\n",
              "      <td>36.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sedan - Liability</td>\n",
              "      <td>3742</td>\n",
              "      <td>32.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sedan - All Perils</td>\n",
              "      <td>3067</td>\n",
              "      <td>26.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sport - Collision</td>\n",
              "      <td>262</td>\n",
              "      <td>2.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Utility - All Perils</td>\n",
              "      <td>251</td>\n",
              "      <td>2.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Utility - Collision</td>\n",
              "      <td>24</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Sport - All Perils</td>\n",
              "      <td>19</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Utility - Liability</td>\n",
              "      <td>13</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Sport - Liability</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             PolicyType  Claims Percentage-%\n",
              "0     Sedan - Collision    4186        36.20\n",
              "1     Sedan - Liability    3742        32.36\n",
              "2    Sedan - All Perils    3067        26.52\n",
              "3     Sport - Collision     262         2.27\n",
              "4  Utility - All Perils     251         2.17\n",
              "5   Utility - Collision      24         0.21\n",
              "6    Sport - All Perils      19         0.16\n",
              "7   Utility - Liability      13         0.11\n",
              "8     Sport - Liability       1         0.01"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sok2hTjW3cpH",
        "outputId": "80ca9755-a8f7-46d0-805a-1abc417b5065"
      },
      "source": [
        "customers[customers['FraudFound_P']==1]['PolicyType'].value_counts()"
      ],
      "id": "Sok2hTjW3cpH",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sedan - All Perils      307\n",
              "Sedan - Collision       281\n",
              "Sport - Collision        34\n",
              "Utility - All Perils     33\n",
              "Sedan - Liability        27\n",
              "Utility - Collision       3\n",
              "Name: PolicyType, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "3999310f",
        "outputId": "9d2a4caf-cc10-433f-e90d-bd062eb75f6f"
      },
      "source": [
        "plt.subplots(figsize=(15, 6))\n",
        "sns.countplot(x='PolicyType',\n",
        "              data=customers[customers['FraudFound_P']==1],\n",
        "              palette='RdYlGn',\n",
        "              order=customers['PolicyType'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Policy Type')\n",
        "plt.title('Policy Type vs Number of Fraud')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = customers[customers['FraudFound_P']==1]['PolicyType'].value_counts().index\n",
        "y = np.array(customers[customers['FraudFound_P']==1]['PolicyType'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum()) * 100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['PolicyType', 'Fraud', 'Percentage-%'])   "
      ],
      "id": "3999310f",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PolicyType</th>\n",
              "      <th>Fraud</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sedan - All Perils</td>\n",
              "      <td>307</td>\n",
              "      <td>44.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sedan - Collision</td>\n",
              "      <td>281</td>\n",
              "      <td>41.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sport - Collision</td>\n",
              "      <td>34</td>\n",
              "      <td>4.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Utility - All Perils</td>\n",
              "      <td>33</td>\n",
              "      <td>4.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sedan - Liability</td>\n",
              "      <td>27</td>\n",
              "      <td>3.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Utility - Collision</td>\n",
              "      <td>3</td>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             PolicyType  Fraud Percentage-%\n",
              "0    Sedan - All Perils    307        44.82\n",
              "1     Sedan - Collision    281        41.02\n",
              "2     Sport - Collision     34         4.96\n",
              "3  Utility - All Perils     33         4.82\n",
              "4     Sedan - Liability     27         3.94\n",
              "5   Utility - Collision      3         0.44"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ceacb557"
      },
      "source": [
        "###From this two graph we can see their more chance of fraud for people having policy type All Perlis and Car Type Sedan or Utility"
      ],
      "id": "ceacb557"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghTu1_CG0860"
      },
      "source": [
        "##Claim and Fraud with respect to the number of Vehicle owned by the customer"
      ],
      "id": "ghTu1_CG0860"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "a41594cf",
        "outputId": "f006998a-4a67-44f0-e993-9f3618c30c4b"
      },
      "source": [
        "plt.subplots(figsize=(15, 6))\n",
        "sns.countplot(x='NumberOfCars',\n",
        "              data=customers,\n",
        "              palette='RdYlGn',\n",
        "              order=customers['NumberOfCars'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Claims')\n",
        "plt.xlabel('Number of Vehicles')\n",
        "plt.title('Number of Vehicles vs Number of Claims')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = customers['NumberOfCars'].value_counts().index\n",
        "y = np.array(customers['NumberOfCars'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum())*100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['Number of Vehicles', 'Claims', 'Percentage-%'])   "
      ],
      "id": "a41594cf",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of Vehicles</th>\n",
              "      <th>Claims</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>10698</td>\n",
              "      <td>92.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2 vehicles</td>\n",
              "      <td>559</td>\n",
              "      <td>4.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3 to 4</td>\n",
              "      <td>288</td>\n",
              "      <td>2.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5 to 8</td>\n",
              "      <td>19</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>more than 8</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Number of Vehicles  Claims Percentage-%\n",
              "0          1 vehicle   10698        92.50\n",
              "1         2 vehicles     559         4.83\n",
              "2             3 to 4     288         2.49\n",
              "3             5 to 8      19         0.16\n",
              "4        more than 8       1         0.01"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "d5e551a7",
        "outputId": "3a23517f-0159-4edb-8b8a-106e423785d7"
      },
      "source": [
        "plt.subplots(figsize=(15, 6))\n",
        "sns.countplot(x='NumberOfCars',\n",
        "              data=customers[customers['FraudFound_P']==1],\n",
        "              palette='RdYlGn',\n",
        "              order=customers['NumberOfCars'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Number of Car')\n",
        "plt.title('Number of Car vs Number of Fraud')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = customers[customers['FraudFound_P']==1]['NumberOfCars'].value_counts().index\n",
        "y = np.array(customers[customers['FraudFound_P']==1]['NumberOfCars'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum()) * 100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['Number of Vehicles', 'Fraud', 'Percentage-%'])   "
      ],
      "id": "d5e551a7",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of Vehicles</th>\n",
              "      <th>Fraud</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>628</td>\n",
              "      <td>91.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2 vehicles</td>\n",
              "      <td>37</td>\n",
              "      <td>5.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3 to 4</td>\n",
              "      <td>19</td>\n",
              "      <td>2.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5 to 8</td>\n",
              "      <td>1</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Number of Vehicles  Fraud Percentage-%\n",
              "0          1 vehicle    628        91.68\n",
              "1         2 vehicles     37         5.40\n",
              "2             3 to 4     19         2.77\n",
              "3             5 to 8      1         0.15"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cC6l3dVE4M96"
      },
      "source": [
        "##Number of Frauds with respect to Relationship Status [Single] and Number of Cars owned"
      ],
      "id": "cC6l3dVE4M96"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "56c2ab2b",
        "outputId": "bbc1df03-8fd6-45ea-af7a-f0116711c58c"
      },
      "source": [
        "plt.subplots(figsize=(15, 6))\n",
        "single = customers[customers['MaritalStatus']=='Single']\n",
        "sns.countplot(x='NumberOfCars',\n",
        "              data=single[single['FraudFound_P']==1],\n",
        "              palette='RdYlGn',\n",
        "              order=single['NumberOfCars'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Number of Vehicle')\n",
        "plt.title('Relationship - Single vs Number of Fraud Done')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "x = single[single['FraudFound_P']==1]['NumberOfCars'].value_counts().index\n",
        "y = np.array(single[single['FraudFound_P']==1]['NumberOfCars'].value_counts().values)\n",
        "perFraud = np.true_divide(y, y.sum()) * 100\n",
        "claim_array = []\n",
        "\n",
        "for i in range(len(perFraud)):\n",
        "    small_claim = []\n",
        "    small_claim.append(x[i])\n",
        "    small_claim.append(y[i])\n",
        "    small_claim.append(format(perFraud[i], '.2f'))\n",
        "    #print(x[i], format(perFraud[i], '.2f'), '%')\n",
        "    claim_array.append(small_claim)\n",
        "    \n",
        "pd.DataFrame(claim_array, columns=['Number of Vehicles', 'Claims', 'Percentage-%'])    "
      ],
      "id": "56c2ab2b",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kSVIPDS3sJflSkmuSnDvQdlCSK5Kc2T6eNbDuXUkuTHJBkqcPqy5JkiRJGgXD7Nn7CvCMCdo/WVXbt4/jAJI8HNgL2Kbd57NJVh1ibZIkSZLUa0MLe1V1EvCnaW6+B/CNqrqtqn4PXAjsPKzaJEmSJKnvurhm7/VJzm6HeW7Qtm0CXDawzeVt299Isl+S05Kctnjx4mHXKkmSJEkrpdkOe58DtgS2BxYBBy/rAarq0KpaWFUL582bN9P1SZIkSVIvzGrYq6qrq+rOqroL+AL3DNW8AnjQwKabtm2SJEmSpOUwq2EvycYDT58PjM3UeQywV5I1kmwObAWcMpu1SZIkSVKfrDasAyc5AtgF2DDJ5cCBwC5JtgcKuATYH6CqzktyJPBb4A7gdVV157BqkyRJkqS+G1rYq6q9J2j+4hTbfwj40LDqkSRJkqRR0sVsnJIkSZKkITPsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPDS3sJflSkmuSnDvQ9s9Jzk9ydpLvJlm/bV+Q5JYkZ7aPzw+rLkmSJEkaBcPs2fsK8IxxbScAj6iqbYH/Ad41sO6iqtq+fRwwxLokSZIkqfeGFvaq6iTgT+Pajq+qO9qnJwObDuv8kiRJkjTKurxm71XAfw083zzJb5KcmOQJXRUlSZIkSX2wWhcnTfKPwB3A19qmRcD8qro2yaOAo5NsU1XXT7DvfsB+APPnz5+tkiVJkiRppTLrPXtJXgE8B9inqgqgqm6rqmvb5dOBi4CHTLR/VR1aVQurauG8efNmqWpJkiRJWrnMathL8gzg7cDuVXXzQPu8JKu2y1sAWwEXz2ZtkiRJktQnQxvGmeQIYBdgwySXAwfSzL65BnBCEoCT25k3nwh8IMlfgbuAA6rqTxMeWJIkSZK0VEMLe1W19wTNX5xk26OAo4ZViyRJkiSNmi5n45QkSZIkDYlhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6aLXJViS5AajJ1lfVukOpSJIkSZK0wiYNe1W1DkCSDwKLgK8CAfYBNp6V6iRJkiRJy2U6wzh3r6rPVtUNVXV9VX0O2GPYhUmSJEmSlt90wt5NSfZJsmqSVZLsA9w07MIkSZIkSctvOmHv/wJ7Ale3jxe1bZIkSZKkOWrSa/bGVNUlOGxTkiRJklYqSw17Sb7MBLNyVtWrhlKRJEmSJGmFLTXsAccOLK8JPB+4cjjlSJIkSZJmwnSGcR41+DzJEcDPh1aRJEmSJGmFTWeClvG2Au4/04VIkiRJkmbOdK7Zu4Hmmr20P68C3jHkuiRJkiRJK2A6wzjXmY1CJEmSJEkzZzoTtJBkA5rhm2uOtVXVScMqSpIkSZK0YqYzjPM1wJuATYEzgccAvwJ2HW5pkiRJkqTlNZ0JWt4E7ARcWlVPBnYA/jzUqiRJkiRJK2Q6Ye/WqroVIMkaVXU+sPVwy5IkSZIkrYjpXLN3eZL1gaOBE5JcB1w63LIkSZIkSStiOrNxPr9dPCjJT4D1gB8MtSpJkiRJ0gqZMuwlWRU4r6oeClBVJ85KVZIkSZKkFTLlNXtVdSdwQZL5s1SPJEmSJGkGTOeavQ2A85KcAtw01lhVuw+tKkmSJEnSCpk07CVZraruAN47i/VIkiRJkmbAVD17pwA7VtWJSf5fVb1htoqSJEmSJK2Yqa7Zy8Dy44ZdiCRJkiRp5kwV9mrWqpAkSZIkzaipwt5Dk5yd5JyB5bOTnJPk7OkcPMmXklyT5NyBtvsmOSHJ/7Y/N2jbk+TTSS5sz7Pjir00SZIkSRpdU12z97AZOP5XgM8Ahw+0vRP4cVV9NMk72+fvAJ4JbNU+Hg18rv0pSZIkSVpGk4a9qrp0RQ9eVSclWTCueQ9gl3b5MOCnNGFvD+Dwqirg5CTrJ9m4qhataB2SJEmSNGqmvKn6kGw0EOCuAjZqlzcBLhvY7vK2bQlJ9ktyWpLTFi9ePNxKJUmSJGkl1UXYu1vbi7dME8FU1aFVtbCqFs6bN29IlUmSJEnSym3SsJfkx+3Pj83wOa9OsnF77I2Ba9r2K4AHDWy3adsmSZIkSVpGU/XsbZzkscDuSXZIsuPgYwXOeQzw8nb55cD3Btpf1s7K+RjgL16vJ0mSJEnLZ6rZON8HvJemh+0T49YVsOvSDp7kCJrJWDZMcjlwIPBR4MgkrwYuBfZsNz8OeBZwIXAz8MppvwpJkiRJ0hKmmo3z28C3k7y3qj64PAevqr0nWbXbBNsW8LrlOY8kSZIkaUlT9ewBUFUfTLI78MS26adVdexwy5IkSZIkrYilzsaZ5CPAm4Dfto83JfnwsAuTJEmSJC2/pfbsAc8Gtq+quwCSHAb8Bnj3MAuTJEmSJC2/6d5nb/2B5fWGUYgkSZIkaeZMp2fvI8BvkvwECM21e+8calWSJEmSpBUynQlajkjyU2CntukdVXXVUKuSJEmSJK2Q6fTs0d7c/Jgh1yJJkiRJmiHTvWZPkiRJkrQSMexJkiRJUg9NGfaSrJrk/NkqRpIkSZI0M6YMe1V1J3BBkvmzVI8kSZIkaQZMZ4KWDYDzkpwC3DTWWFW7D60qSZIkSdIKmU7Ye+/Qq5AkSZIkzajp3GfvxCSbAVtV1Y+SrAWsOvzSJEmSJEnLa6mzcSbZF/g2cEjbtAlw9DCLkiRJkiStmOnceuF1wOOA6wGq6n+B+w+zKEmSJEnSiplO2Lutqm4fe5JkNaCGV5IkSZIkaUVNJ+ydmOTdwL2TPBX4FvD94ZYlSZIkSVoR0wl77wQWA+cA+wPHAe8ZZlGSJEmSpBUzndk470pyGPBrmuGbF1SVwzglSZIkaQ5bathL8mzg88BFQIDNk+xfVf817OIkSZIkSctnOjdVPxh4clVdCJBkS+A/AcOeJEmSJM1R07lm74axoNe6GLhhSPVIkiRJkmbApD17SV7QLp6W5DjgSJpr9l4EnDoLtUmSJEmSltNUwzifO7B8NfCkdnkxcO+hVSRJkiRJWmGThr2qeuVsFiJJkiRJmjnTmY1zc+ANwILB7atq9+GVJUmSJElaEdOZjfNo4IvA94G7hluOJEmSJGkmTCfs3VpVnx56JZIkSZKkGTOdsPevSQ4EjgduG2usqjOGVpUkSZIkaYVMJ+w9EngpsCv3DOOs9rkkSZIkaQ6aTth7EbBFVd0+7GIkSZIkSTNjlWlscy6w/rALkSRJkiTNnOn07K0PnJ/kVJa8Zs9bL0iSJEnSHDWdsHfgTJ4wydbANweatgDeRxMq9wUWt+3vrqrjZvLckiRJkjQqlhr2qurEmTxhVV0AbA+QZFXgCuC7wCuBT1bVv8zk+SRJkiRpFC017CW5gWb2TYB7AasDN1XVujNw/t2Ai6rq0iQzcDhJkiRJEkxjgpaqWqeq1m3D3b2B/wN8dobOvxdwxMDz1yc5O8mXkmwwQ+eQJEmSpJEzndk471aNo4Gnr+iJk9wL2B34Vtv0OWBLmiGei4CDJ9lvvySnJTlt8eLFE20iSZIkSSNvOsM4XzDwdBVgIXDrDJz7mcAZVXU1wNjP9pxfAI6daKeqOhQ4FGDhwoU10TaSJEmSNOqmMxvncweW7wAuAfaYgXPvzcAQziQbV9Wi9unzae7vJ0mSJElaDtOZjfOVM33SJGsDTwX2H2j+eJLtaSaDuWTcOkmSJEnSMpg07CV53xT7VVV9cHlPWlU3Afcb1/bS5T2eJEmSJGlJU/Xs3TRB29rAq2mC2nKHPUmSJEnScE0a9qrq7tkwk6wDvInmxuffYJKZMiVJkiRJc8OU1+wluS/wFmAf4DBgx6q6bjYKkyRJkiQtv6mu2ftn4AU0tzl4ZFXdOGtVSZIkSZJWyFQ3Vf8H4IHAe4Ark1zfPm5Icv3slCdJkiRJWh5TXbM3VRCUJEmSJM1hBjpJkiRJ6iHDniRJkiT1kGFPkiRJknrIsCdJkiRJPWTYkyRJkqQeMuxJkiRJUg8Z9iRJkiSphwx7kiRJktRDhj1JkiRJ6iHDniRJkiT1kGFPkiRJknrIsCdJkiRJPWTYkyRJkqQeMuxJkiRJUg8Z9iRJkiSphwx7kiRJktRDhj1JkiRJ6iHDniRJkiT1kGFPkiRJknrIsCdJkiRJPWTYkyRJkqQeMuxJkiRJUg8Z9iRJkiSphwx7kiRJktRDhj1JkiRJ6iHDniRJkiT1kGFPkiRJknrIsCdJkiRJPWTYkyRJkqQeWq2rEye5BLgBuBO4o6oWJrkv8E1gAXAJsGdVXddVjZIkSZK0suq6Z+/JVbV9VS1sn78T+HFVbQX8uH0uSZIkSVpGXYe98fYADmuXDwOe12EtkiRJkrTS6jLsFXB8ktOT7Ne2bVRVi9rlq4CNuilNkiRJklZunV2zBzy+qq5Icn/ghCTnD66sqkpS43dqg+F+APPnz5+dSiVJkiRpJdNZz15VXdH+vAb4LrAzcHWSjQHan9dMsN+hVbWwqhbOmzdvNkuWJEmSpJVGJ2EvydpJ1hlbBp4GnAscA7y83ezlwPe6qE+SJEmSVnZdDePcCPhukrEavl5VP0hyKnBkklcDlwJ7dlSfJEmSJK3UOgl7VXUxsN0E7dcCu81+RZIkSZLUL3Pt1guSJEmSpBlg2JMkSZKkHjLsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6yLAnSZIkST1k2JMkSZKkHjLsSZIkSVIPzXrYS/KgJD9J8tsk5yV5U9t+UJIrkpzZPp4127VJkiRJUl+s1sE57wD+oarOSLIOcHqSE9p1n6yqf+mgJkmSJEnqlVkPe1W1CFjULt+Q5HfAJrNdhyRJkiT1WafX7CVZAOwA/Lpten2Ss5N8KckGk+yzX5LTkpy2ePHiWapUkiRJklYunYW9JPcBjgLeXFXXA58DtgS2p+n5O3ii/arq0KpaWFUL582bN2v1SpIkSdLKpJOwl2R1mqD3tar6DkBVXV1Vd1bVXcAXgJ27qE2SJEmS+qCL2TgDfBH4XVV9YqB944HNng+cO9u1SZIkSVJfdDEb5+OAlwLnJDmzbXs3sHeS7YECLgH276A2SZIkSeqFLmbj/DmQCVYdN9u1SJIkSVJfdTobpyRJkiRpOAx7kiRJktRDhj1JkiRJ6iHDniRJkiT1kGFPkiRJknrIsCdJkiRJPWTYkyRJkqQeMuxJkiRJUg8Z9iRJkiSphwx7kiRJktRDhj1JkiRJ6iHDniRJkiT1kGFPkiRJknpota4LkDSxa//7A12XoDnofru+r+sSJEnSSsKePUmSJEnqIcOeJEmSJPWQYU+SJEmSesiwJ0mSJEk9ZNiTJEmSpB4y7EmSJElSDxn2JEmSJKmHDHuSJEmS1EOGPUmSJEnqIcOeJEmSJPWQYU+SJEmSesiwJ0mSJEk9ZNiTJEmSpB5aresCJEkrn1tu/VXXJWgOuveaf9d1CZKkAfbsSZIkSVIPGfYkSZIkqYcMe5IkSZLUQ4Y9SZIkSeohw54kSZIk9ZBhT5IkSZJ6aAQxVNAAABCbSURBVM6FvSTPSHJBkguTvLPreiRJkiRpZTSnwl6SVYF/A54JPBzYO8nDu61KkiRJklY+cyrsATsDF1bVxVV1O/ANYI+Oa5IkSZKklc5cC3ubAJcNPL+8bZMkSZIkLYPVui5gWSXZD9ivfXpjkgu6rKdnNgT+2HURc8Kh6boCLcnP5t0O7LoA/S0/n5qr/GxqLvPzOXM2m2zFXAt7VwAPGni+adt2t6o6FDh0NosaFUlOq6qFXdchjednU3OZn0/NVX42NZf5+Zwdc20Y56nAVkk2T3IvYC/gmI5rkiRJkqSVzpzq2auqO5K8HvghsCrwpao6r+OyJEmSJGmlM6fCHkBVHQcc13UdI8rhsZqr/GxqLvPzqbnKz6bmMj+fsyBV1XUNkiRJkqQZNteu2ZMkSZIkzQDDniRJkiT1kGFPkqTllOT+XdcgTSTJhl3XII1JstrA8n2SLExy3y5rGhWGvRGWZK0k703yhfb5Vkme03VdUpKPJ1k3yepJfpxkcZKXdF2XRluS+4573A84JckGfmlRl5I8M8nvk/w8yQ5JzgN+neTyJLt1XZ9GW5JXAFcn+Z8kzwTOBj4GnJVk706LGwFO0DLCknwTOB14WVU9IslawC+ravuOS9OIS3JmVW2f5PnAc4C3ACdV1XYdl6YRluQu4NJxzZsClwNVVVvMflVS828msDewPnAs8OyqOjnJw4CvVdWOnRaokZbkHODJwDrAWcAOVXVRko2AE6pq204L7Dl79kbbllX1ceCvAFV1M5BuS5KAe24L82zgW1X1ly6LkVpvAy4Adq+qzatqc+Dydtmgpy7dVVW/q6pfATdX1ckAVfU7/K6n7t1ZVX+sqt8DN1bVRQBVdXXHdY2EOXefPc2q25PcGyiAJFsCt3VbkgTAsUnOB24BXptkHnBrxzVpxFXVwe2IiE8muQw4kPbfT6ljf06yP7AucF2SvweOBJ4C3NhpZRL8IclHaHr2zk9yMPAdms/nok4rGwH+tme0HQj8AHhQkq8BPwbe3m1JElTVO4HHAgur6q/AzcAe3VYlQVVdXlUvAn4KnACs1W1FEgAvB3YEtgCe1rb9ENgT2LeroqTWS4DraYa87w78EngXcH/gFd2VNRq8Zm/EtRMMPIZm+ObJVfXHjkuSaK8ffQswv6r2S7IVsHVVHdtxadLd2pERW1bVuV3XIknSRAx7IyjJlBdqV9UZs1WLNBEnD5IkSVpxXrM3mg6eYl0Bu85WIdIktqyqF49NyVxVNydx8iBJkqRlYNgbQVX15K5rkJbCyYMkSZJWkGFvhCV5Hc39d/7cPt8A2LuqPtttZdLfTB70OLyIW3NEktWB1wJPbJtOBD7fTiYkdSbJesBBwBPaphOBD3j7Gs0F7cza+wILGMggVfWqrmoaBV6zN8LGblw9ru03VbVDVzVJY5w8SHNVkn8HVgcOa5teSnMfqdd0V5UESY4CzmXJz+Z2VfWC7qqSGkl+CfyM5pr8O8faq+qozooaAYa9EZbkHGDbaj8ESVYFzq6qbbqtTKPKyYO0MkhyVlVtt7Q2abZN8kvcv2mTuuBnsRsO4xxtPwC+meSQ9vn+bZvUFScP0srgziRbVtVFAEm2YOC31FKHbkny+Kr6OUCSxwG3dFyTNObYJM+qquO6LmSU2LM3wpKsQhPwdmubTgD+var80iJJk0iyG/Bl4GKaYcabAa+qqv/utDCNvCTbAYcD67VN1wEvr6qzu6tKaiS5AVibZsK1v9L8+1lVtW6nhfWcYU/SnOPkQZrLkqzRLm7d/rwAoKqcMVadSrJ5Vf0+yboAVXX9WFvXtUnqhmFvBCU5sqr2bK/Z+5sPQFVt20FZ0t2cPEhzWZIzqmrHpbVJs22Sz+bpVfWormqSBrW/vN0KWHOsrapO6q6i/vOavdH0pvbnczqtQprcqkkybvKge3Vck0ZckgcAmwD3TrIDzRAkgHWBtTorTCMvyUOBbYD1kgzOvLkuA1+qpS4leQ3Nd9BNgTNpZtz+FV6PP1SGvRFUVYvan5d2XYs0CScP0lz0dJr7PW5KM5nQWNi7Hnh3RzVJ0Awpfg6wPvDcgfYbaO5rJs0FbwJ2ormd0pPbX1J8uOOaes9hnCOs/e3fx4D703xp8UJZzQlOHqS5LMn/8b5QmouS/F1V/arrOqSJJDm1qnZKcibw6Kq6Lcl53vJruAx7IyzJhcBzq+p3XdciSZKk/kryXeCVwJtphm5eB6xeVc/qtLCeM+yNsCS/qKrHdV2HNMbJgyRJ6r8kT6K5RcgPqur2ruvpM8PeCBq4ePtJwAOAo2nueQJAVX2ni7qkJBtX1aIkm0203utMJUlaebUTrm3EwLwhVfWH7irqP8PeCEry5SlWV1W9ataKkaSVSJKdaf6dPDXJw4FnAOdX1XEdlybdLcnjgZ2Bc6vq+K7rkQCSvAE4ELgauKttLkftDJdhT9Kc4+RBmouSHAg8k+Y30icAjwZ+AjwV+GFVfajD8jTCkpxSVTu3y/sCrwO+CzwN+H5VfbTL+iS4e66IR1fVtV3XMkoMeyMsyUOAzwEbVdUjkmwL7F5V/9RxaRpxTh6kuai9lnR7YA3gKmDTqro+yb2BX/vbaXUlyW+qaod2+VTgWVW1OMnaNNPcP7LbCiVI8hPgqVV1R9e1jBLvszfavgC8DTgEoKrOTvJ1wLCnrl1t0NMcdEd7+4+bk1xUVdcDVNUtSe5ayr7SMK2SZANgFZpf5C8GqKqbkvjFWp1K8pZ28WLgp0n+kyXnivhEJ4WNCMPeaFurqk5JMtjmfwrqzMDkQacl+SZOHqS55fYka1XVzcCjxhqTrMc9159IXVgPOJ12yPvAZFf3adukLq3T/vxD+7hX+4AJZt7WzDLsjbY/JtmS9i9akhcCi7otSSPuuQPLN9NcbzKmAMOeuvTEqroNoKoGw93qwMu7KUmCqlowyaq7gOfPYinS36iq9wMkeVFVfWtwXZIXdVPV6PCavRGWZAvgUOCxNDe2/D2wj9PbS5IkaSYlOaOqdlxam2aWPXuj7dKqekp7AfcqVXVD1wVJ4ORBkiT1RZJnAs8CNkny6YFV6+LlQ0O3StcFqFO/T3Io8Bjgxq6LkQZ8AXgX8FdoJg8C9uq0IkmStDyuBE4DbqW5tnTscQzw9A7rGgkO4xxhSdYCnkPzJXpH4FjgG1X1804L08hLcmpV7TRuOvEzq2r7rmuTJEnLLsnqVfXXrusYNfbsjbCqurmqjqyqFwA70HSnn9hxWRI4eZAkSb1i0OuG1+yNuCRPAl4MPIOmi33PbiuSAHgdzeRBD01yBe3kQd2WJEmStHJxGOcIS3IJ8BvgSOCYqrqp24qkRpJVq+pOJw+SJKlfBu5XqlngMM7Rtm1VPb+qjjDoaY5x8iBJknokyWOT/BY4v32+XZLPdlxW7xn2RlhVXd91DdIkHgr8iGY45++TfCbJ4zuuSZIkLb9P0sy+eS1AVZ0FPLHTikaAYU/SnOPkQZIk9U9VXTau6c5OChkhhj1Jc1KSJ7XDO04H1sTJgyRJWpldluSxQCVZPclbgd91XVTfOUGLlpDklVX15a7r0Ghz8iBJkvolyYbAvwJPAQIcD7ypqq7ttLCeM+xpCUn+UFXzu65Doy3Jul5TKklSPyRZFTi8qryN0izzPnsjKMnZk60CNprNWqSJGPQkSeqP9nZKmyW5V1Xd3nU9o8SwN5o2opkN6bpx7QF+OfvlSJIkqecuBn6R5Bjg7sszquoT3ZXUf4a90XQscJ+qOnP8iiQ/nf1yJEmS1HMXtY9VgHU6rmVkeM2epDklyUOBTYBfV9WNA+3PqKofdFeZJElaUUnuAzD4f7yGx1svSJozkrwR+B7wBuDcJHsMrP5wN1VJkqQVleQRSX4DnAecl+T0JNt0XVffOYxT0lyyL/CoqroxyQLg20kWVNW/0lxTKkmSVk6HAm+pqp8AJNkF+ALw2C6L6jvDnqS5ZJWxYR1VdUn7H8G3k2yGYU+SpJXZ2mNBD6Cqfppk7S4LGgUO45Q0l1ydZPuxJ23wew6wIfDIzqqSJEkr6uIk702yoH28h2aGTg2RE7RImjOSbArcUVVXTbDucVX1iw7KkiRJKyjJBsD7gce3TT8DDqqq8bcC0wwy7EmSJElSD3nNniRJkqShSrIQeDewgIEMUlXbdlXTKLBnT5IkSdJQJbkAeBtwDnDXWHtVXdpZUSPAnj1JkiRJw7a4qo7puohRY8+eJEmSpKFKshuwN/Bj4Lax9qr6TmdFjQB79iRJkiQN2yuBhwKrc88wzgIMe0Nkz54kSZKkoUpyQVVt3XUdo8abqkuSJEkatl8meXjXRYwae/YkSZIkDVWS3wFbAr+nuWYvQHnrheEy7EmSJEkaqiSbTdTurReGy7AnSZIkST3kNXuSJEmS1EOGPUmSJEnqIcOeJGnOSFJJDh54/tYkB83Qsb+S5IUzcaylnOdFSX6X5Cfj2i9OsvW4tk8leccUx7pxkvYDkrxsiv0WJDl3WWuXJPWLYU+SNJfcBrwgyYZdFzIoyWrLsPmrgX2r6snj2r8B7DVwzFWAF7bty6SqPl9Vhy/rfpKk0WLYkyTNJXcAhwJ/P37F+J65sV6vJLskOTHJ99res48m2SfJKUnOSbLlwGGekuS0JP+T5Dnt/qsm+eckpyY5O8n+A8f9WZJjgN9OUM/e7fHPTfKxtu19wOOBLyb553G7HAG8eOD5E4FLq+rSJC9p6z0zySFJVh04z4eSnJXk5CQbtW0HJXlru/zgJD9qtzlj3Oud9PVJkvrPsCdJmmv+DdgnyXrLsM92wAHAw4CXAg+pqp2BfwfeMLDdAmBn4NnA55OsSdMT95eq2gnYCdg3yebt9jsCb6qqhwyeLMkDgY8BuwLbAzsleV5VfQA4Ddinqt42uE9VnQPclWS7tmkv4IgkD6MJgY+rqu2BO4F92m3WBk6uqu2Ak4B9J3jtXwP+rd3mscCiceunen2SpB4z7EmS5pSquh44HHjjMux2alUtqqrbgIuA49v2c2gC3pgjq+quqvpf4GLgocDTgJclORP4NXA/YKt2+1Oq6vcTnG8n4KdVtbiq7qAJXE+cRp1HAHu1w0KfB3wL2A14FHBqW8NuwBbt9rcDx7bLp497LSRZB9ikqr4LUFW3VtXN48451euTJPXYslyDIEnSbPkUcAbw5YG2O2h/Sdle73avgXW3DSzfNfD8Lpb8v278zWULCPCGqvrh4IokuwA3LV/5k/oGTRA9ETi7qq5OEuCwqnrXBNv/te65Ie6dLN//2xO+PklS/9mzJ0mac6rqT8CRNEMQx1xC0wMGsDuw+nIc+kVJVmmva9sCuAD4IfDaJKsDJHlIkrWXcpxTgCcl2bC9vm5vmgA3paq6CPgj8FGaXj6AHwMvTHL/9vz3TbLZdF5MVd0AXJ7kee2+ayRZa9xmy/P6JEk9YNiTJM1VBwODs3J+gSZgnQX8HcvX6/YHmqD2X8ABVXUrzXV9vwXOaG9XcAhL6UGrqkXAO4GfAGcBp1fV96ZZwxE0w0e/0x7rt8B7gOOTnA2cAGy8DK/ppcAb231/CTxg3Pplfn2SpH7IPaNDJEmSJEl9Yc+eJEmSJPWQYU+SJEmSesiwJ0mSJEk9ZNiTJEmSpB4y7EmSJElSDxn2JEmSJKmHDHuSJEmS1EOGPUmSJEnqof8PHg/gRs2xigsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of Vehicles</th>\n",
              "      <th>Claims</th>\n",
              "      <th>Percentage-%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>178</td>\n",
              "      <td>89.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2 vehicles</td>\n",
              "      <td>14</td>\n",
              "      <td>7.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3 to 4</td>\n",
              "      <td>6</td>\n",
              "      <td>3.03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Number of Vehicles  Claims Percentage-%\n",
              "0          1 vehicle     178        89.90\n",
              "1         2 vehicles      14         7.07\n",
              "2             3 to 4       6         3.03"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eca3c3a0"
      },
      "source": [
        "## Claims Amount, Vehicle Price | Claim Size Percent"
      ],
      "id": "eca3c3a0"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1457a75",
        "outputId": "169c9157-30d5-49d0-e054-d73b9b9839e1"
      },
      "source": [
        "customers['VehiclePrice'].value_counts()"
      ],
      "id": "a1457a75",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20000 to 29000     6112\n",
              "30000 to 39000     2640\n",
              "more than 69000    1605\n",
              "less than 20000     800\n",
              "40000 to 59000      349\n",
              "60000 to 69000       59\n",
              "Name: VehiclePrice, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d504159"
      },
      "source": [
        "vehicle_price_dict = {\n",
        "    'less than 20000': 20000,\n",
        "    '20000 to 29000': 29000,\n",
        "    '30000 to 39000': 39000,\n",
        "    '40000 to 59000': 59000,\n",
        "    '60000 to 69000': 69000,\n",
        "    'more than 69000': 100000\n",
        "}\n",
        "\n",
        "claim_trustability = []\n",
        "\n",
        "for index, row in customers.iterrows():\n",
        "    ct = row['ClaimSize']/vehicle_price_dict.get(row['VehiclePrice']) * 100\n",
        "    claim_trustability.append(ct)\n",
        "\n",
        "customers['Claim_Amount_Percent'] = claim_trustability"
      ],
      "id": "7d504159",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eed90a30",
        "outputId": "bb7c0c83-7089-489d-88ba-5fe58e8adc6e"
      },
      "source": [
        "claim_amount_100 = customers[customers['Claim_Amount_Percent']>100]\n",
        "claim_amount_100['VehiclePrice'].value_counts()"
      ],
      "id": "eed90a30",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30000 to 39000     1128\n",
              "20000 to 29000      178\n",
              "40000 to 59000       76\n",
              "60000 to 69000       23\n",
              "less than 20000       5\n",
              "Name: VehiclePrice, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "09af6004",
        "outputId": "bf75e64c-2d99-443a-f71f-92926399d0af"
      },
      "source": [
        "claim_amount_100.head()"
      ],
      "id": "09af6004",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>WeekOfMonth</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>Make</th>\n",
              "      <th>AccidentArea</th>\n",
              "      <th>DayOfWeekClaimed</th>\n",
              "      <th>MonthClaimed</th>\n",
              "      <th>WeekOfMonthClaimed</th>\n",
              "      <th>Sex</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fault</th>\n",
              "      <th>PolicyType</th>\n",
              "      <th>VehicleCategory</th>\n",
              "      <th>VehiclePrice</th>\n",
              "      <th>FraudFound_P</th>\n",
              "      <th>PolicyNumber</th>\n",
              "      <th>RepNumber</th>\n",
              "      <th>Deductible</th>\n",
              "      <th>DriverRating</th>\n",
              "      <th>Days_Policy_Accident</th>\n",
              "      <th>Days_Policy_Claim</th>\n",
              "      <th>PastNumberOfClaims</th>\n",
              "      <th>AgeOfVehicle</th>\n",
              "      <th>AgeOfPolicyHolder</th>\n",
              "      <th>PoliceReportFiled</th>\n",
              "      <th>WitnessPresent</th>\n",
              "      <th>AgentType</th>\n",
              "      <th>NumberOfSuppliments</th>\n",
              "      <th>AddressChange_Claim</th>\n",
              "      <th>NumberOfCars</th>\n",
              "      <th>Year</th>\n",
              "      <th>BasePolicy</th>\n",
              "      <th>ClaimSize</th>\n",
              "      <th>Claim_Amount_Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Mar</td>\n",
              "      <td>5</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Mar</td>\n",
              "      <td>5</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>52.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sedan - Liability</td>\n",
              "      <td>Sport</td>\n",
              "      <td>20000 to 29000</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>400</td>\n",
              "      <td>1.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>2 to 4</td>\n",
              "      <td>more than 7</td>\n",
              "      <td>41 to 50</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>none</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Liability</td>\n",
              "      <td>103420.785275</td>\n",
              "      <td>356.623397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Mar</td>\n",
              "      <td>3</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>Jun</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sedan - Collision</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>30000 to 39000</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>400</td>\n",
              "      <td>2.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 4</td>\n",
              "      <td>5 years</td>\n",
              "      <td>26 to 30</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>more than 5</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Collision</td>\n",
              "      <td>84964.753582</td>\n",
              "      <td>217.858343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Mar</td>\n",
              "      <td>2</td>\n",
              "      <td>Friday</td>\n",
              "      <td>Dodge</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Mar</td>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>47.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sedan - Collision</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>30000 to 39000</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>400</td>\n",
              "      <td>1.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>2 to 4</td>\n",
              "      <td>more than 7</td>\n",
              "      <td>41 to 50</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Internal</td>\n",
              "      <td>none</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Collision</td>\n",
              "      <td>54233.789823</td>\n",
              "      <td>139.061000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>May</td>\n",
              "      <td>4</td>\n",
              "      <td>Friday</td>\n",
              "      <td>Pontiac</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Friday</td>\n",
              "      <td>May</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>49.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sedan - Liability</td>\n",
              "      <td>Sport</td>\n",
              "      <td>30000 to 39000</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>14</td>\n",
              "      <td>400</td>\n",
              "      <td>2.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 4</td>\n",
              "      <td>more than 7</td>\n",
              "      <td>41 to 50</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>none</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Liability</td>\n",
              "      <td>52494.337422</td>\n",
              "      <td>134.600865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Jun</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Pontiac</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Jun</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>Single</td>\n",
              "      <td>45.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sedan - Collision</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>30000 to 39000</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>8</td>\n",
              "      <td>400</td>\n",
              "      <td>1.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>1</td>\n",
              "      <td>7 years</td>\n",
              "      <td>36 to 40</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>1 to 2</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>Collision</td>\n",
              "      <td>58488.924392</td>\n",
              "      <td>149.971601</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Month  WeekOfMonth DayOfWeek  ... BasePolicy      ClaimSize Claim_Amount_Percent\n",
              "7    Mar            5    Monday  ...  Liability  103420.785275           356.623397\n",
              "18   Mar            3  Thursday  ...  Collision   84964.753582           217.858343\n",
              "21   Mar            2    Friday  ...  Collision   54233.789823           139.061000\n",
              "35   May            4    Friday  ...  Liability   52494.337422           134.600865\n",
              "38   Jun            4   Tuesday  ...  Collision   58488.924392           149.971601\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "215b526d",
        "outputId": "162de8c8-2f2b-4b93-a1ee-e370d41b9fcb"
      },
      "source": [
        "claim_amount_100[claim_amount_100['FraudFound_P']==1]['VehiclePrice'].value_counts()"
      ],
      "id": "215b526d",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30000 to 39000    57\n",
              "20000 to 29000     9\n",
              "40000 to 59000     5\n",
              "Name: VehiclePrice, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "cc98db7d",
        "outputId": "1481517a-e930-4ed6-d68e-d8fae48600fd"
      },
      "source": [
        "claim_amount_100_vehicle = claim_amount_100[claim_amount_100['VehiclePrice']!='more than 69000']\n",
        "fraud_claim = claim_amount_100_vehicle[claim_amount_100_vehicle['FraudFound_P']==1]\n",
        "\n",
        "plt.subplots(figsize=(10, 6))\n",
        "\n",
        "sns.countplot(x='VehiclePrice',\n",
        "              data=fraud_claim,\n",
        "              palette='RdYlGn',\n",
        "              order=fraud_claim['VehiclePrice'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Vehicle Price ')\n",
        "plt.title('Vehicle Price vs Number of Fraud Done (Claim Percent>100%)')\n",
        "\n",
        "plt.show()"
      ],
      "id": "cc98db7d",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cba084d8"
      },
      "source": [
        "###Vehicle having price between 30000 to 39000 and whose claim percent is greater than 100 has more chance of commiting fraud"
      ],
      "id": "cba084d8"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbmxS-Sc9Zg2"
      },
      "source": [
        "##Frauds and Claims made with respect to Age of Vehicle, Relationship Status [Single]"
      ],
      "id": "VbmxS-Sc9Zg2"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eda684ba",
        "outputId": "4fa6a943-7f5a-4c93-90c6-4521ffec32a6"
      },
      "source": [
        "customers['AgeOfPolicyHolder'].value_counts()"
      ],
      "id": "eda684ba",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31 to 35    4199\n",
              "36 to 40    3008\n",
              "41 to 50    2122\n",
              "51 to 65    1070\n",
              "26 to 30     445\n",
              "over 65      388\n",
              "16 to 17     247\n",
              "21 to 25      73\n",
              "18 to 20      13\n",
              "Name: AgeOfPolicyHolder, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "ad64992a",
        "outputId": "cf3d061c-d3e8-40a9-e26c-aff0e5134ad7"
      },
      "source": [
        "single = customers[customers['MaritalStatus']=='Single']\n",
        "single = single[single['AgeOfPolicyHolder']=='31 to 35']\n",
        "fraud = single[single['FraudFound_P']==1]\n",
        "\n",
        "plt.subplots(figsize=(10, 6))\n",
        "\n",
        "sns.countplot(x='AgeOfVehicle',\n",
        "              data=fraud,\n",
        "              palette='RdYlGn',\n",
        "              order=fraud['AgeOfVehicle'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Age of Vehicle')\n",
        "plt.title('Age of Vehicle - Relationship Status [Single] vs Number of Fraud Done')\n",
        "\n",
        "plt.show()"
      ],
      "id": "ad64992a",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C+F+6y8FfZ5bwvUAn0V0aPh7456r6ehs/1PtrNu0Y9ga6s81X0Z0xf9YqqHe6rwFfBf6Pbj/+NSteipz1NWvv4afR9d26lq5f7Bfm2d6eSW6i21dPpAt5e1TVD9s6V/p40f5pfgHd35rr6NrlefMtpxVNfbpGi1ySdwF3raoD5p1ZWqAkl9J1Kl9oIF9ttTMp59P9oXxVVf3niLd3JN2HR960wOUeSxce7gQ86Q4EZ0k9WK2+RkWrTru8uC7dWaA96C4Pju1O59KkaZcDh7pn0spIsgfdWYxL6M787gu8c6HrqarjWfFyn6TVmMFr8dqY7vLUtnR9YN5D9xF6SauHu9JdNtqC7pYML66qM+ZeRNKk81KjJElST+xcL0mS1BODlyRJUk8moo/XlltuWTvvvPO4y5AkSZrX6aef/rOq2mqmaRMRvHbeeWdOO+20+WeUJEkasySzfrWTlxolSZJ6YvCSJEnqicFLkiSpJwYvSZKknhi8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ4YvCRJknpi8JIkSeqJwUuSJKknBi9JkqSeGLwkSZJ6sva4C1jVznnhfuMuYdG774eOGXcJkiRNJM94SZIk9cTgJUmS1BODlyRJUk8MXpIkST0xeEmSJPXE4CVJktQTg5ckSVJPDF6SJEk9MXhJkiT1xOAlSZLUE4OXJElSTwxekiRJPTF4SZIk9cTgJUmS1BODlyRJUk8MXpIkST0xeEmSJPXE4CVJktQTg5ckSVJPDF6SJEk9MXhJkiT1xOAlSZLUE4OXJElSTwxekiRJPVl7lCtPcilwI3AbcGtVLU2yOXAksDNwKfDMqrpulHVIkiStDvo44/WYqtqtqpa2x68Fjq+qewLHt8eSJEmL3jguNe4LHN6GDwf2G0MNkiRJvRt18Crg60lOT3JwG7d1VV3Vhq8Gtp5pwSQHJzktyWnLli0bcZmSJEmjN9I+XsAjquonSe4CHJfkvMGJVVVJaqYFq+pQ4FCApUuXzjiPJEnSJBnpGa+q+kn7/VPgaOAhwDVJtgFov386yhokSZJWFyMLXkk2TLLx1DDweOBs4FjggDbbAcAXR1WDJEnS6mSUlxq3Bo5OMrWdT1fVV5OcCnw2yUHAZcAzR1iDJEnSamNkwauqLgYeOMP4nwOPHdV2JUmSVlfeuV6SJKknBi9JkqSeGLwkSZJ6YvCSJEnqicFLkiSpJwYvSZKknhi8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ4YvCRJknpi8JIkSeqJwUuSJKknBi9JkqSeGLwkSZJ6YvCSJEnqicFLkiSpJwYvSZKknhi8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ4YvCRJknpi8JIkSeqJwUuSJKknBi9JkqSeGLwkSZJ6YvCSJEnqicFLkiSpJwYvSZKknhi8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ4YvCRJknpi8JIkSeqJwUuSJKknBi9JkqSeGLwkSZJ6YvCSJEnqicFLkiSpJwYvSZKknhi8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ4YvCRJknoy8uCVZEmSM5J8uT3eJckpSS5McmSSdUddgyRJ0uqgjzNehwDnDjx+F/CvVXUP4DrgoB5qkCRJGruRBq8k2wNPBj7cHgfYC/hcm+VwYL9R1iBJkrS6GPUZr38DXg3c3h5vAVxfVbe2x1cA2424BkmSpNXCyIJXkqcAP62q01dy+YOTnJbktGXLlq3i6iRJkvo3yjNeDwf2SXIp8Bm6S4zvBTZNsnabZ3vgJzMtXFWHVtXSqlq61VZbjbBMSZKkfowseFXV31XV9lW1M/As4JtVtT9wAvD0NtsBwBdHVYMkSdLqZBz38XoN8PIkF9L1+TpsDDVIkiT1bu35Z7njqupE4MQ2fDHwkD62K0mStDrxzvWSJEk9MXhJkiT1xOAlSZLUE4OXJElSTwxekiRJPTF4SZIk9cTgJUmS1BODlyRJUk8MXpIkST0xeEmSJPXE4CVJktQTg5ckSVJPDF6SJEk9MXhJkiT1xOAlSZLUE4OXJElSTwxekiRJPTF4SZIk9WTt2SYkuRGo2aZX1Z1HUpEkSdIiNWvwqqqNAZK8FbgK+AQQYH9gm16qkyRJWkSGudS4T1V9oKpurKobquqDwL6jLkySJGmxGSZ43Zxk/yRLkqyVZH/g5lEXJkmStNgME7yeAzwTuKb9PKONkyRJ0gLM2sdrSlVdipcWJUmS7rB5g1eSjzLDpxur6i9HUpEkSdIiNW/wAr48MLwe8CfAlaMpR5IkafEa5lLj5wcfJzkC+M7IKpIkSVqkVubO9fcE7rKqC5EkSVrshunjNXUH+7TfVwOvGXFdkiRJi84wlxo37qMQSZKkxW6YzvUk2YzuEuN6U+Oq6lujKkqSJGkxGuZS4/OBQ4DtgTOBhwEnA3uNtjRJkqTFZZjO9YcAewCXVdVjgN2B60dalSRJ0iI0TPD6dVX9GiDJnarqPODeoy1LkiRp8Rmmj9cVSTYFjgGOS3IdcNloy5IkSVp8hvlU45+0wTcnOQHYBPjqSKuSJElahOYMXkmWAOdU1X0AquqkXqqSJElahObs41VVtwHnJ9mxp3okSZIWrWH6eG0GnJPk+8DNUyOrap+RVSVJkrQIzRq8kqxdVbcCb+ixHkmSpEVrrjNe3wceVFUnJXlfVb20r6IkSZIWo7n6eGVg+OGjLkSSJGmxmyt4VW9VSJIkrQHmutR4nyRn0Z35unsbpj2uqnrAyKuTJElaROYKXn/QWxWSJElrgFmDV1X5tUCSJEmr0DBfki1JkqRVwOAlSZLUk1mDV5Lj2+93rcyKk6yX5PtJ/jfJOUne0sbvkuSUJBcmOTLJuitXuiRJ0mSZ64zXNkn+ENgnye5JHjT4M8S6fwPsVVUPBHYDnpDkYcC7gH+tqnsA1wEH3dEnIUmSNAnm+lTjG+m+Lmh74F+mTStgr7lWXFUF3NQertN+ppZ7Tht/OPBm4IMLKVqSJGkSzfWpxs8Bn0vyhqp668qsPMkS4HTgHsD7gYuA69t3QAJcAWw3y7IHAwcD7LjjjiuzeUmSpNXKXGe8AKiqtybZB/ijNurEqvryMCuvqtuA3ZJsChwN3GfYwqrqUOBQgKVLl3oXfUmSNPHm/VRjkncAhwA/aj+HJPnHhWykqq4HTgD2BDZNMhX4tgd+sqCKJUmSJtQwt5N4MvC4qvpIVX0EeALwlPkWSrJVO9NFkvWBxwHn0gWwp7fZDgC+uDKFS5IkTZp5LzU2mwLXtuFNhlxmG+Dw1s9rLeCzVfXlJD8CPpPkbcAZwGELKViSJGlSDRO83gGckeQEui/I/iPgtfMtVFVnAbvPMP5i4CELrFOSJGniDdO5/ogkJwJ7tFGvqaqrR1qVJEnSIjTUpcaqugo4dsS1SJIkLWp+V6MkSVJPDF6SJEk9mTN4JVmS5Ly+ipEkSVrM5gxe7c7z5yfxO3skSZLuoGE6128GnJPk+8DNUyOrap+RVSVJkrQIDRO83jDyKiRJktYAw9zH66QkOwH3rKpvJNkAWDL60iRJkhaXYb4k+wXA54APtVHbAceMsihJkqTFaJjbSbwEeDhwA0BVXQDcZZRFSZIkLUbDBK/fVNVvpx4kWRuo0ZUkSZK0OA0TvE5K8jpg/SSPA44CvjTasiRJkhafYYLXa4FlwA+BFwJfAV4/yqIkSZIWo2E+1Xh7ksOBU+guMZ5fVV5qlCRJWqB5g1eSJwP/AVwEBNglyQur6r9HXZwkSdJiMswNVN8DPKaqLgRIcnfgvwCDlyRJ0gIM08frxqnQ1VwM3DiieiRJkhatWc94JXlaGzwtyVeAz9L18XoGcGoPtUmSJC0qc11qfOrA8DXAo9rwMmD9kVUkSZK0SM0avKrqwD4LkSRJWuyG+VTjLsBLgZ0H56+qfUZXliRJ0uIzzKcajwEOo7tb/e2jLUeSJGnxGiZ4/bqq/n3klUjAz7/5D+MuYdHbYq83jrsESVpjDRO83pvkTcDXgd9MjayqH4ysKkmSpEVomOB1f+C5wF4sv9RY7bEkSZKGNEzwegZwt6r67aiLkSRJWsyGuXP92cCmoy5EkiRpsRvmjNemwHlJTmXFPl7eTkKSJGkBhglebxp5FZIkSWuAeYNXVZ3URyGSJEmL3TB3rr+R7lOMAOsC6wA3V9WdR1mYJEnSYjPMGa+Np4aTBNgXeNgoi5IkSVqMhvlU4+9U5xjgj0dUjyRJ0qI1zKXGpw08XAtYCvx6ZBVJkiQtUsN8qvGpA8O3ApfSXW6UJEnSAgzTx+vAPgqRJEla7GYNXkneOMdyVVVvHUE9kiRJi9ZcZ7xunmHchsBBwBaAwUuSJGkBZg1eVfWeqeEkGwOHAAcCnwHeM9tykiRJmtmcfbySbA68HNgfOBx4UFVd10dhkiRJi81cfbzeDTwNOBS4f1Xd1FtVkiRJi9BcN1B9BbAt8HrgyiQ3tJ8bk9zQT3mSJEmLx1x9vBZ0V3tJkiTNzXAlSZLUk2HuXC9J8/rVr08edwmL3vrr7TnuEiTdQZ7xkiRJ6snIgleSHZKckORHSc5Jckgbv3mS45Jc0H5vNqoaJEmSViejPON1K/CKqtoVeBjwkiS7Aq8Fjq+qewLHt8eSJEmL3siCV1VdVVU/aMM3AucC2wH70t2MlfZ7v1HVIEmStDrppY9Xkp2B3YFTgK2r6qo26Wpg6z5qkCRJGreRB68kGwGfB15WVSvceLWqCqhZljs4yWlJTlu2bNmoy5QkSRq5kQavJOvQha5PVdUX2uhrkmzTpm8D/HSmZavq0KpaWlVLt9pqq1GWKUmS1ItRfqoxwGHAuVX1LwOTjgUOaMMHAF8cVQ2SJEmrk1HeQPXhwHOBHyY5s417HfBO4LNJDgIuA545whokSZJWGyMLXlX1HSCzTH7sqLYrSZK0uvLO9ZIkST0xeEmSJPXE4CVJktQTg5ckSVJPDF6SJEk9MXhJkiT1xOAlSZLUE4OXJElSTwxekiRJPTF4SZIk9cTgJUmS1BODlyRJUk8MXpIkST0xeEmSJPXE4CVJktQTg5ckSVJPDF6SJEk9MXhJkiT1xOAlSZLUE4OXJElSTwxekiRJPTF4SZIk9cTgJUmS1BODlyRJUk8MXpIkST0xeEmSJPXE4CVJktQTg5ckSVJPDF6SJEk9MXhJkiT1xOAlSZLUE4OXJElSTwxekiRJPTF4SZIk9cTgJUmS1BODlyRJUk8MXpIkST0xeEmSJPXE4CVJktQTg5ckSVJPDF6SJEk9MXhJkiT1xOAlSZLUE4OXJElSTwxekiRJPTF4SZIk9cTgJUmS1BODlyRJUk9GFrySfCTJT5OcPTBu8yTHJbmg/d5sVNuXJEla3YzyjNfHgCdMG/da4PiquidwfHssSZK0RhhZ8KqqbwHXThu9L3B4Gz4c2G9U25ckSVrd9N3Ha+uquqoNXw1sPduMSQ5OclqS05YtW9ZPdZIkSSM0ts71VVVAzTH90KpaWlVLt9pqqx4rkyRJGo2+g9c1SbYBaL9/2vP2JUmSxqbv4HUscEAbPgD4Ys/blyRJGptR3k7iCOBk4N5JrkhyEPBO4HFJLgD2bo8lSZLWCGuPasVV9exZJj12VNuUJElanXnnekmSpJ4YvCRJknpi8JIkSeqJwUuSJKknBi9JkqSeGLwkSZJ6YvCSJEnqicFLkiSpJwYvSZKknhi8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ4YvCRJknpi8JIkSeqJwUuSJKknBi9JkqSeGLwkSZJ6YvCSJEnqicFLkiSpJwYvSZKknhi8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ4YvCRJknpi8JIkSeqJwUuSJKknBi9JkqSeGLwkSZJ6YvCSJEnqicFLkiSpJwYvSZKknhi8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ4YvCRJknpi8JIkSeqJwUuSJKkna4+7AEnS+P3X2W8ddwmL3pPv94Zxl6DVgGe8JEmSemLwkiRJ6onBS5IkqScGL0mSpJ7YuV6SpAn27E8ePO4S1ghH/Pmhq2Q9nvGSJEnqyViCV5InJDk/yYVJXjuOGiRJkvrWe/BKsgR4P/BEYFfg2Ul27bsOSd2icfsAAAs3SURBVJKkvo3jjNdDgAur6uKq+i3wGWDfMdQhSZLUq3EEr+2AywceX9HGSZIkLWqpqn43mDwdeEJVPb89fi7w0Kr662nzHQxMfVTj3sD5vRbary2Bn427CK0U226y2X6Ty7abbIu9/Xaqqq1mmjCO20n8BNhh4PH2bdwKqupQYNV8dnM1l+S0qlo67jq0cLbdZLP9JpdtN9nW5PYbx6XGU4F7JtklybrAs4Bjx1CHJElSr3o/41VVtyb5a+BrwBLgI1V1Tt91SJIk9W0sd66vqq8AXxnHtldTa8Ql1UXKtptstt/ksu0m2xrbfr13rpckSVpT+ZVBkiRJPTF4SZIk9cTgNQZJ7p7kTm340Un+Jsmm465L87PtJpvtN7mSbJhkrTZ8ryT7JFln3HVpOLbfcgav8fg8cFuSe9B1MNwB+PR4S9KQbLvJZvtNrm8B6yXZDvg68FzgY2OtSAth+zUGr/G4vapuBf4EeF9VvQrYZsw1aTi23WSz/SZXquqXwNOAD1TVM4D7jrkmDc/2awxe43FLkmcDBwBfbuPWyFOuE8i2m2y23+RKkj2B/YH/auOWjLEeLYzt1xi8xuNAYE/g7VV1SZJdgE+MuSYNx7abbLbf5DoE+Dvg6Ko6J8ndgBPGXJOGZ/s13serZ0mWAB+vqv3HXYsWxrabbLbf5Gpt966qeuW4a9HC2X4r8oxXz6rqNmCn9j2VmiC23WSz/SZXa7tHjLsOrRzbb0Vj+cogcTHwP0mOBW6eGllV/zK+kjQk226y2X6T64zWbkexYtt9YXwlaQFsv8bgNR4XtZ+1gI3HXIsWxrabbLbf5FoP+Dmw18C4Ata4P9wTyvZr7OMlSZLUE894jUGSrYBX093DZL2p8VW116wLabVg2002229yJVkPOIjfb7u/HFtRGprtt5yd68fjU8B5wC7AW4BLgVPHWZCGZttNNttvcn0CuCvwx8BJwPbAjWOtSAth+zVeahyDJKdX1YOTnFVVD2jjTq2qPcZdm+Zm2002229yJTmjqnafarv2PX/frqqHjbs2zc/2W85LjeNxS/t9VZInA1cCm4+xHg3Ptptstt/kmmq765PcD7gauMsY69HC2H6NwWs83pZkE+AVwPuAOwN/O96SNCTbbrLZfpPr0CSbAW8AjgU2At443pK0ALZf46VGSZKknti5fgyS3CvJ8UnObo8fkOT1465L87PtJpvtN7mSbJ3ksCT/3R7vmuSgcdel4dh+yxm8xuM/6b4s9BaAqjoLeNZYK9KwbLvJZvtNro8BXwO2bY//D3jZ2KrRQn0M2w8weI3LBlX1/Wnjbh1LJVoo226y2X6Ta8uq+ixwO0BV3QrcNt6StAC2X2PwGo+fJbk73dclkOTpwFXjLUlDsu0mm+03uW5OsgXL2+5hwC/GW5IWwPZr7Fw/BknuBhwK/CFwHXAJsH9VXTbWwjQv226y2X6TK8mDgX8H7gecDWwFPL1dLtZqzvZbzuA1BkmWVNVtSTYE1qqqNfLuvZPItptstt9kS7I2cG8gwPlVdcs8i2g1Yvt1vNQ4HhckeTewowf+iWPbTTbbb0IlOR04GLiyqs5eU/9oTyrbbzmD13g8kO4THYcl+V6Sg5PcedxFaSi23WSz/SbXnwHbAacm+UySP06ScRelodl+jZcaxyzJo4BPA5sCnwPeWlUXjrcqDcO2m2y232RKshbwFOCDdJ+K+yjw3qq6dqyFaSi2n2e8xiLJkiT7JDka+DfgPcDdgC8BXxlrcZqTbTfZbL/JluQBdG32buDzwDOAG4BvjrMuDcf26/hdjeNxAXAC8O6q+u7A+M8l+aMx1aTh2HaTzfabUK2P0PXAYcBrq+o3bdIpSR4+vso0DNtvOS81jkGSjarqpnHXoYWz7Sab7Te5ktytqi4edx1aObbfcgYvSZKkntjHS5IkqScGL0mSpJ4YvHqU5KFT9wxKsn6StyT5UpJ3Jdlk3PVpdkn+JskO465Dq0aSRyR5eZLHj7sWLVySj4+7Bg0vyUOS7NGGd23vvSeNu65xsY9Xj5KcAzywqm5NcijwS7r7Bz22jX/aWAvUrJL8ArgZuAg4AjiqqpaNtyoNK8n3q+ohbfgFwEuAo4HHA1+qqneOsz7NLsmx00cBj6HdgqCq9um9KA0tyZuAJ9LdReE44KF0nyx+HPC1qnr7GMsbC4NXj5KcW1V/0IZ/UFUPGph2ZlXtNr7qNJckZwAPBvamuwPzPsDpdCHsC379zOotyRlVtXsbPhV4UlUta9/Z+L2quv94K9RskvwA+BHwYaDogtcRwLMAquqk8VWn+ST5IbAbcCfgamD7qrohyfrAKVX1gLEWOAZeauzX2UkObMP/m2QpQJJ7AWvs91ZNiKqq26vq61V1ELAt8AHgCYAfkV79rZVksyRb0P3DuQygqm4Gbh1vaZrHUrp/cv4e+EVVnQj8qqpOMnRNhFur6raq+iVwUVXdAFBVvwJuH29p4+ENVPv1fOC9SV4P/Aw4OcnlwOVtmlZfK3ynWPuC12OBY5NsMJ6StACb0P3xDlBJtqmqq5JsxLS21eqlqm4H/jXJUe33Nfi3a5L8NskGLXg9eGpk69e8RgYvLzWOQetgvwvdweOKqrpmzCVpHknuVVX/N+46tGq10Lx1VV0y7lo0nCRPBh5eVa8bdy2aX5I7DdylfnD8lsA2VfXDMZQ1VgYvSZKkntjHS5IkqScGL0mSpJ4YvCT1Lsl+SSrJfXrY1lZJTklyRpJHDox/U5J3TJt3tyTnzrGujyV5+gzjt03yuXnqOHHqk8yS1lwGL0nj8GzgO+33qD0W+GFV7V5V3x4YfwTdPdkGPauNX5CqurKqfi+QSdJ0Bi9JvWq3cHgEcBDtJpht/FpJPpDkvCTHJfnK1NmlJA9OclKS05N8Lck2M6x35yTfTHJWkuOT7JhkN+CfgH2TnNlu2ghA+5TqdUkeOrCaZwJHJLl7kq+27X172pm5P0ry3SQXD9S3c5Kz2/CSJP+c5OxWy0tnqPXxSU5O8oMkR7XXRNIawOAlqW/7Al9twefnSabu7fM0YGdgV+C5wJ4ASdYB3gc8vaoeDHwEmOlrRt4HHN7uhP0p4N+r6kzgjcCRVbVbu2njoN/dAT3Jw4Brq+oC4FDgpW17r6S7We6UbeiC41OAmb5q6OD2PHYbqOV32sfoXw/s3b694jTg5TO/VJIWG29CJ6lvzwbe24Y/0x6fThdmjmo3zLw6yQltnnsD9wOOSwKwBLhqhvXuSRfeAD5Bd6ZrPkcC303yCtplxnb26Q+Bo9r2oPu6kynHtBp/lGTrGda5N/AfVXUrQFVdO236w+jC5f+09a8LnDxErZIWAYOXpN4k2RzYC7h/kqILUZXkVXMtBpxTVXuu6nqq6vIklwCPAv6ULrytBVw/x3enDt4McmXueh/guKrqo3+bpNWMlxol9enpwCeqaqeq2rmqdgAuAR4J/A/wp62v19bAo9sy5wNbJfndpcck951h3d9leZ+x/YFvzzDPTI4A/hW4uKquaN8ld0mSZ7TtJckDF/AcjwNemGTttvzm06Z/D3h4knu06Ru272uVtAYweEnq07OBo6eN+3wb/3ngCuBHwCeBH9B9KfJv6QLbu5L8L3Am3aXA6V4KHJjkLLo+YocMWdNRwH1Z8dOM+wMHte2dQ9cvbVgfBn4MnNWWf87gxPYF3c+ju6x5Ft1lxpHfVkPS6sGvDJK02kiyUVXdlGQL4Pt038l39bjrkqRVxT5eklYnX06yKV2H87cauiQtNp7xkiRJ6ol9vCRJknpi8JIkSeqJwUuSJKknBi9JkqSeGLwkSZJ6YvCSJEnqyf8HCNlQRUuPaz0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "97c2ac67",
        "outputId": "260db0af-8f08-40a9-9e86-0deb094b3f24"
      },
      "source": [
        "single = customers[customers['MaritalStatus']=='Single']\n",
        "single = single[single['AgeOfPolicyHolder']=='31 to 35']\n",
        "fraud = single[single['FraudFound_P']==1]\n",
        "\n",
        "plt.subplots(figsize=(10, 6))\n",
        "\n",
        "sns.countplot(x='AgeOfVehicle',\n",
        "              data=single,\n",
        "              palette='RdYlGn',\n",
        "              order=single['AgeOfVehicle'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Claim')\n",
        "plt.xlabel('Age of Vehicle')\n",
        "plt.title('Age of Vehicle - Relationship Status [Single] vs Number of Claim Done')\n",
        "\n",
        "plt.show()"
      ],
      "id": "97c2ac67",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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acVfF5/YzdFtVjpr0D/kFwEVtl9xLW12zqqoL6dYtg5d8+BTwv3S79r7ONGF9JZ1It6v6eODdVfX11n+o79d02jrsLXRbs6+g2yL/vJWY/j10Z7++me5H1iV0692prrk222d4VXtda5Or27JPAx5dd15G6TC69+rbwIV0eyReNuS859XuuvNsImkFSd4F3LOq9p11ZGklJbmI7iD6lQ3wvdW2GPyM7p/Ya6vq3xd4eZ+jO1nm4JWcbne6sLEO8NR5BG1Ji6BXt8nR6LTdnWvTbWV6BN3uypFdyV4aN2335HTXgpu3JI+g2/p7Id2W5b3pzsxbKVV1PCvufpTUYwY1TdiQbnfZVnTH8LyH7pIEkvrhnnTHkm5Gd4mLv6qq02eeRNK4c9enJElST3kygSRJUk8Z1CRJknpqSR6jtvnmm9fy5ctHXYYkSdKsTjvttF9V1bKphi3JoLZ8+XJOPfXU2UeUJEkasSQXTzfMXZ+SJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FMGNUmSpJ4yqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FNrjbqAUTj5WU8adQkj9agvHjfqEiRJ0hDcoiZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8tWFBLcliSXyY5a6DfpkmOS3Ju+7tJ658kH0xyXpIzkzxsYJp92/jnJtl3oeqVJEnqm4XcovZJ4CmT+r0BOL6qdgCOb88B9gR2aI8DgI9AF+yAg4FHAY8EDp4Id5IkSUvdggW1qvo2cM2k3nsDh7fuw4FnDvQ/ojonARsn2RL4E+C4qrqmqq4FjuOu4U+SJGlJWuxj1Laoqita95XAFq17a+CSgfEubf2m6y9JkrTkjexkgqoqoFbV/JIckOTUJKdeddVVq2q2kiRJI7PYQe0XbZcm7e8vW//LgG0Hxtum9Zuu/11U1SFVtUtV7bJs2bJVXrgkSdJiW+ygdgwwcebmvsBXBvq/sJ39uSvw67aL9H+AJyfZpJ1E8OTWT5Ikaclba6FmnORI4AnA5kkupTt7853A55PsD1wMPLeNfizwVOA84GZgP4CquibJ24BT2nj/UFWTT1CQJElakhYsqFXV86cZtPsU4xZw4DTzOQw4bBWWJkmSNBa8M4EkSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeqpkQS1JK9M8pMkZyU5Msm6SbZPcnKS85J8Lsnabdx12vPz2vDlo6hZkiRpsS16UEuyNfByYJeqehCwJvA84F3A+6rqvsC1wP5tkv2Ba1v/97XxJEmSlrxR7fpcC1gvyVrA+sAVwBOBo9vww4Fntu6923Pa8N2TZBFrlSRJGolFD2pVdRnwbuDndAHt18BpwHVVdVsb7VJg69a9NXBJm/a2Nv5mk+eb5IAkpyY59aqrrlrYFyFJkrQIRrHrcxO6rWTbA1sBdweeMt/5VtUhVbVLVe2ybNmy+c5OkiRp5Eax63MP4MKquqqqbgW+CDwG2LjtCgXYBrisdV8GbAvQhm8EXL24JUuSJC2+UQS1nwO7Jlm/HWu2O3A28C3g2W2cfYGvtO5j2nPa8G9WVS1ivZIkSSMximPUTqY7KeBHwI9bDYcArwdeleQ8umPQDm2THAps1vq/CnjDYtcsSZI0CmvNPsqqV1UHAwdP6n0B8Mgpxv0t8JzFqEuSJKlPvDOBJElSTxnUJEmSesqgJkmS1FMGNUmSpJ4yqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FMGNUmSpJ4yqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElST6012whJ1gSeBiwfHL+q3rtwZUmSJGnWoAZ8Ffgt8GPg9oUtR5IkSROGCWrbVNVOC16JJEmSVjDMMWpfS/LkBa9EkiRJKxhmi9pJwJeSrAHcCgSoqrrHglYmSZK0mhsmqL0X2A34cVXVAtcjSZKkZphdn5cAZxnSJEmSFtcwW9QuAE5I8jXglomeXp5DkiRpYQ0T1C5sj7XbQ5IkSYtg1qBWVX+/GIVIkiRpRdMGtSTvr6pXJPkqcJfj06pqrwWtTJIkaTU30xa1T7W/716MQiRJkrSiaYNaVZ3W/p64eOVIkiRpwjA3Zd8BeAewI7DuRP+quvcC1iVJkrTaG+Y6ap8APgLcBvwxcATwHwtZlCRJkoYLautV1fFAquriqvo74GkLW5YkSZKGuY7aLe0+n+cm+RvgMmCDhS1LkiRJw2xROwhYH3g58HDgBcC+C1mUJEmShrvg7Smt80Zgv4UtR5IkSRNmuuDtlBe6neAFbyVJkhbWTFvUvNCtJEnSCM0U1M4GllXV2YM9k+wIXLWgVUmSJGnGkwk+BGw+Rf/NgA8sTDmSJEmaMFNQu29VfXtyz6r6DrDTwpUkSZIkmDmobTjDsLut6kIkSZK0opmC2nlJnjq5Z5I9gQsWriRJkiTBzCcTvAL4ryTPBU5r/XYBdgOevtCFSZIkre6m3aJWVecCDwZOBJa3x4nATlX1f4tRnCRJ0upsxjsTVNUtwCcWqRZJkiQNGOZen5IkSRoBg5okSVJPTRvUkhzf/r5rVS80ycZJjk7y0yTnJNktyaZJjktybvu7SRs3ST6Y5LwkZyZ52KquR5IkqY9m2qK2ZZJHA3sleWiShw0+5rncDwD/XVUPAB4CnAO8ATi+qnYAjm/PAfYEdmiPA4CPzHPZkiRJY2GmkwneCrwF2AZ476RhBTxxLgtMshHwR8CLAKrqd8DvkuwNPKGNdjhwAvB6YG/giKoq4KS2NW7LqrpiLsuXJEkaF9MGtao6Gjg6yVuq6m2rcJnb093U/RNJHkJ3jbaDgC0GwteVwBate2vgkoHpL239VghqSQ6g2+LGve51r1VYriRJ0mjMejJBVb0tyV5J3t0e873Y7VrAw4CPVNVDgZu4czfnxDKLbqvd0KrqkKrapap2WbZs2TxLlCRJGr1Zg1qSd9Bt8Tq7PQ5K8k/zWOalwKVVdXJ7fjRdcPtFki3bMrcEftmGXwZsOzD9Nq2fJEnSkjbM5TmeBjypqg6rqsOApzCPW0hV1ZXAJUnu33rtThcAjwH2bf32Bb7Suo8BXtjO/twV+LXHp0mSpNXBjHcmGLAxcE3r3mgVLPdlwKeTrE13g/f96ELj55PsD1wMPLeNeyzwVOA84OY2riRJ0pI3TFB7B3B6km8BoTtj8w0zTzKzqjqD7gbvk+0+xbgFHDif5UmSJI2jWYNaVR2Z5ATgEa3X69vuS0mSJC2goXZ9tmPCjlngWiRJkjTAe31KkiT1lEFNkiSpp2YMaknWTPLTxSpGkiRJd5oxqFXV74GfJfGeTJIkSYtsmJMJNgF+kuSHdLd7AqCq9lqwqiRJkjRUUHvLglchSZKkuxjmOmonJtkO2KGqvpFkfWDNhS9NkiRp9TbMTdlfTHfj9I+1XlsDX17IoiRJkjTc5TkOBB4DXA9QVecCf7CQRUmSJGm4oHZLVf1u4kmStYBauJIkSZIEwwW1E5O8EVgvyZOAo4CvLmxZkiRJGiaovQG4Cvgx8BLgWODNC1mUJEmShjvr8/YkhwMn0+3y/FlVuetTkiRpgc0a1JI8DfgocD4QYPskL6mqry10cZIkSauzYS54+x7gj6vqPIAk9wH+CzCoSZIkLaBhjlG7YSKkNRcANyxQPZIkSWqm3aKW5Fmt89QkxwKfpztG7TnAKYtQmyRJ0mptpl2fzxjo/gXw+NZ9FbDeglUkSZIkYIagVlX7LWYhGh+Xffiloy5hpLY+8KOjLkGStJoY5qzP7YGXAcsHx6+qvRauLEmSJA1z1ueXgUPp7kZw+8KWI0mSpAnDBLXfVtUHF7wSSZIkrWCYoPaBJAcDXwdumehZVT9asKokSZI0VFB7MPAC4Incueuz2nNJkiQtkGGC2nOAe1fV7xa6GEmSJN1pmDsTnAVsvNCFSJIkaUXDbFHbGPhpklNY8Rg1L88hSZK0gIYJagcveBWSJEm6i1mDWlWduBiFSJIkaUXD3JngBrqzPAHWBu4G3FRV91jIwiRJklZ3w2xR23CiO0mAvYFdF7IoSZIkDXfW5x2q82XgTxaoHkmSJDXD7Pp81sDTNYBdgN8uWEWSJEkChjvr8xkD3bcBF9Ht/pQkSdICGuYYtf0WoxBJkiStaNqgluStM0xXVfW2BahHkiRJzUxb1G6aot/dgf2BzQCDmiRJ0gKaNqhV1XsmupNsCBwE7Ad8FnjPdNNJkiRp1ZjxGLUkmwKvAvYBDgceVlXXLkZhkiRJq7uZjlH7F+BZwCHAg6vqxkWrSpIkSTNe8PbVwFbAm4HLk1zfHjckuX5xypMkSVp9zXSM2krdtUCSJEmrlmFMkiSppwxqkiRJPWVQkyRJ6qmRBbUkayY5Pcl/tufbJzk5yXlJPpdk7dZ/nfb8vDZ8+ahqliRJWkyj3KJ2EHDOwPN3Ae+rqvsC19LdAYH299rW/31tPEmSpCVvJEEtyTbA04CPt+cBnggc3UY5HHhm6967PacN372NL0mStKSNaova+4HXAbe355sB11XVbe35pcDWrXtr4BKANvzXbXxJkqQlbdGDWpKnA7+sqtNW8XwPSHJqklOvuuqqVTlrSZKkkRjFFrXHAHsluYjuBu9PBD4AbJxk4gK82wCXte7LgG0B2vCNgKsnz7SqDqmqXapql2XLli3sK5AkSVoEix7Uqupvq2qbqloOPA/4ZlXtA3wLeHYbbV/gK637mPacNvybVVWLWLIkSdJI9Ok6aq8HXpXkPLpj0A5t/Q8FNmv9XwW8YUT1SZIkLapp7/W5GKrqBOCE1n0B8Mgpxvkt8JxFLUySJKkH+rRFTZIkSQMMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6aq1RFyCtbq4944OjLmFkNtn55aMuQZLGilvUJEmSesqgJkmS1FMGNUmSpJ4yqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FMGNUmSpJ4yqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FMGNUmSpJ4yqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FMGNUmSpJ4yqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FOLHtSSbJvkW0nOTvKTJAe1/psmOS7Jue3vJq1/knwwyXlJzkzysMWuWZIkaRRGsUXtNuDVVbUjsCtwYJIdgTcAx1fVDsDx7TnAnsAO7XEA8JHFL1mSJGnxLXpQq6orqupHrfsG4Bxga2Bv4PA22uHAM1v33sAR1TkJ2DjJlotctiRJ0qIb6TFqSZYDDwVOBraoqivaoCuBLVr31sAlA5Nd2vpNntcBSU5NcupVV121YDVLkiQtlpEFtSQbAF8AXlFV1w8Oq6oCamXmV1WHVNUuVbXLsmXLVlVhoiUAAA9kSURBVGGlkiRJozGSoJbkbnQh7dNV9cXW+xcTuzTb31+2/pcB2w5Mvk3rJ0mStKSN4qzPAIcC51TVewcGHQPs27r3Bb4y0P+F7ezPXYFfD+wilSRJWrLWGsEyHwO8APhxkjNavzcC7wQ+n2R/4GLguW3YscBTgfOAm4H9FrdcSZKk0Vj0oFZV3wUyzeDdpxi/gAMXtChJkqQe8s4EkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPbXWqAuQpGH95rc/GHUJI7XeuruNugRJi8wtapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUU2Nz1meSpwAfANYEPl5V7xxxSZI0Vn5w/vtHXcJI7XafV4y6BGmljUVQS7Im8GHgScClwClJjqmqs0dbmSRpdfHP33rNqEsYqdf98btHXcJqaVx2fT4SOK+qLqiq3wGfBfYecU2SJEkLaiy2qAFbA5cMPL8UeNSIapEkSSvp0R98/qhLGJnvv/zIOU+bqlqFpSyMJM8GnlJVf9mevwB4VFX9zcA4BwAHtKf3B3626IUOb3PgV6MuYozZfvNj+82dbTc/tt/82H5z1/e2266qlk01YFy2qF0GbDvwfJvW7w5VdQhwyGIWNVdJTq2qXUZdx7iy/ebH9ps7225+bL/5sf3mbpzbblyOUTsF2CHJ9knWBp4HHDPimiRJkhbUWGxRq6rbkvwN8D90l+c4rKp+MuKyJEmSFtRYBDWAqjoWOHbUdawiY7GLtsdsv/mx/ebOtpsf229+bL+5G9u2G4uTCSRJklZH43KMmiRJ0mrHoCZJktRTBrVFkOQ+SdZp3U9I8vIkG4+6rnFh+82P7Td3Se6eZI3Wfb8keyW526jrGhe2n0ZlKa33DGqL4wvA75Pcl+6Axm2Bz4y2pLFi+82P7Td33wbWTbI18HXgBcAnR1rReLH95inJfUZdw5haMus9g9riuL2qbgP+P+BDVfVaYMsR1zRObL/5sf3mLlV1M/As4N+q6jnAA0dc0zix/ebvsCTnJ/lskgOTPHjUBY2JJbPeM6gtjluTPB/YF/jP1s/N/8Oz/ebH9pu7JNkN2Af4r9ZvzRHWM25sv3mqqscDfwh8CNgY+K8k14y2qrGwZNZ7Y3MdtTG3H/BS4B+r6sIk2wOfGnFN48T2mx/bb+4OAv4W+FJV/STJvYFvjbimcWL7zVOSxwKPa4+N6ULHd0Za1HhYMus9r6O2wJKsCRxRVfuMupZxZPvNj+03d63t3lVVrxl1LePI9ls1ktwGnAa8Azi2qn434pJ6b6mt99z1ucCq6vfAdu0epVpJtt/82H5z19rusaOuY1zZfqvM5sA/ALsB/53kG0neNuKaem2prffc9bk4LgC+l+QY4KaJnlX13tGVNFZsv/mx/ebu9NZuR7Fi231xdCWNFdtvnqrquiQX0J21uA3waMb0WKtFtmTWewa1xXF+e6wBbDjiWsaR7Tc/tt/crQtcDTxxoF8BBo3h2H7z1ELaT4HvAh8B9nP351CWzHrPY9QkSeqpJGtU1e2jrkOj4xa1RZBkGfA6uusHrTvRv6qeOO1EuoPtNz+239wlWRfYn7u23V+MrKgxYvutEvdN8hFgi6p6UJKdgL2q6u2jLqzPltJ6z5MJFsen6TZdbw/8PXARcMooCxoztt/82H5z9yngnsCfACfSHSN0w0grGi+23/z9O90lTm4FqKozgeeNtKLxsGTWe+76XARJTquqhyc5s6p2av1OqapHjLq2cWD7zY/tN3dJTq+qh060XbtP5XeqatdR1zYObL/5m/iuTrRl63dGVe086tr6bCmt99z1uThubX+vSPI04HJg0xHWM25sv/mx/eZuou2uS/Ig4ErgD0ZYz7ix/ebvV+1+nwWQ5NnAFaMtaSwsmfWeQW1xvD3JRsCr6W4Dcg/glaMtaazYfvNj+83dIUk2Ad4CHANsALx1tCWNFdtv/g6ku6n4A5JcBlxId0suzWzJrPfc9SlJUk8lWQd4NrCcbovQ9UBV1T+Msi4tHk8mWARJ7pfk+CRntec7JXnzqOsaF7bf/Nh+c5dkiySHJvlae75jkv1HXde4sP1Wia8Az6DblXc5cCMDF3DV1JbSes8taosgyYnAa4GPDRwMelZVPWi0lY0H229+bL+5awHjE8CbquohSdYCTq+qB4+4tLFg+82f39W5WUrrPbeoLY71q+qHk/rdNpJKxpPtNz+239xtXlWfB24HqKrbgN+PtqSxYvvN3/eTGGxX3pJZ73kyweLwrJ35sf3mx/abu5uSbMadbbcr8OvRljRWbL/5eyzwoiQXArcAoTtGbafRltV7S2a9567PRZDk3nRn7TwauJZ21k5VXTzSwsaE7Tc/tt/cJXk48EHgQcBZwDLg2e2io5qF7Td/Sbabqr/f35ktpfWeQW0RJFmzqn6f5O7AGlXllblXgu03P7bf/LTjqu5PtyXjZ1V16yyTaIDtp1FYSus9j1FbHOcm+RfgXuP8YRkh229+bL85SnIacABweVWdZchYObafRmjJrPcMaovjIcD/AYcmOSnJAUnuMeqixojtNz+239z9GbA1cEqSzyb5kyQZdVFjxPbTqCyZ9Z67PhdZkscDnwE2Bo4G3lZV5422qvFh+82P7Tc3SdYAng58hO6sxU8AH6iqa0Za2Jiw/TRK477ec4vaIkiyZpK9knwJeD/wHuDewFeBY0da3Biw/ebH9pufJDvRtdm/AF8AnkN3dfhvjrKucWH7aRSW0nrPy3MsjnOBbwH/UlXfH+h/dJI/GlFN48T2mx/bb47aMVbXAYcCb6iqW9qgk5M8ZnSVjQfbTyO0ZNZ77vpcBEk2qKobR13HuLL95sf2m7sk966qC0Zdx7iy/TQqS2m9Z1CTJEnqKY9RkyRJ6imDmiRJUk8Z1BZQkkdNXLclyXpJ/j7JV5O8K8lGo66v75K8PMm2o65jqUjy2CSvSvLkUdcyjpIcMeoaxkmSRyZ5ROvesX32njrqurT0JXlAkt2TbDCp/1NGVdN8eIzaAkryE+AhVXVbkkOAm+mu4bJ76/+skRbYc0l+DdwEnA8cCRxVVVeNtqrxkeSHVfXI1v1i4EDgS8CTga9W1TtHWV+fJTlmci/gj2mXlKiqvRa9qDGS5GBgT7orCxwHPIruDLwnAf9TVf84wvK0hCV5Od267hxgZ+CgqvpKG/ajqnrYKOubC4PaAkpyTlX9Yete4QOS5Iyq2nl01fVfktOBhwN70F3hfC/gNLrQ9sVxvy3IQktyelU9tHWfAjy1qq5q9747qaoePNoK+yvJj4CzgY8DRRfUjgSeB1BVJ46uuv5L8mO6f5LrAFcC21TV9UnWA06uqp1GWqCWrPbZ262qbkyynG7jyKeq6gOD68Rx4q7PhXVWkv1a9/8m2QUgyf0A73k3u6qq26vq61W1P7AV8G/AUwBP+Z/dGkk2SbIZ3Y+yqwCq6ibgttGW1nu70P0oeBPw66o6AfhNVZ1oSBvKbVX1+6q6GTi/qq4HqKrfALePtjQtcWtMXJajqi4CngDsmeS9dD+4xo5BbWH9JfD4JOcDOwI/SHIB8O9tmGa2wpeqqm6tqmOq6vnAdiOqaZxsRBc2TgU2TbIldNcXYkxXWIul/UB4H7Af8KYk/4oXCF8Zv0uyfut++ETPdmyuQU0L6RdJ7thb1ULb04HNgbHci+Cuz0XQTijYnm5Ff2lV/WLEJY2FJPerqv8bdR1LTfsHukVVXTjqWsZFkqcBj6mqN466lnGQZJ2BuxAM9t8c2LKqfjyCsrQaSLIN3RbdK6cY9piq+t4IypoXg5okSVJPuetTkiSppwxqkiRJPWVQkzQWkjwzSSV5wCIsa1mSk5OcnuRxA/0PTvKOSePunOScGeb1ySTPnqL/VkmOnqWOEybOFpe0ejKoSRoXzwe+2/4utN2BH1fVQ6vqOwP9j6S7pt+g57X+K6WqLq+quwQ4SRpkUJPUe+2SIo8F9qdddLb1XyPJvyX5aZLjkhw7sfUqycOTnJjktCT/M3F5kknzXZ7km0nOTHJ8knu1U/v/Gdg7yRntIq0AtLOQr03yqIHZPBc4Msl9kvx3W953Jm35+6Mk309ywUB9y5Oc1brXTPLuJGe1Wl42Ra1PTvKDJD9KctTk2+NIWpoMapLGwd7Af7egdHWSiWtzPQtYTnedwhcAuwEkuRvwIeDZVfVw4DBgqtsWfQg4vF0p/9PAB6vqDOCtwOeqaud2kdZBd9yhIMmuwDVVdS5wCPCytrzX0F2cecKWdEHz6cBUt+46oL2OnQdquUO7rMWbgT3aHU5OBV41dVNJWkq8gKOkcfB84AOt+7Pt+Wl04eeoqroduDLJt9o49wceBByXBGBN4Iop5rsbXdgD+BTdlrTZfA74fpJX03Z7tq1bjwaOasuD7vZJE77cajw7yRZTzHMP4KNVdRtAVV0zafiudGH0e23+awM/GKJWSWPOoCap15JsCjwReHCSogtdleS1M00G/KSqdlvV9VTVJUkuBB4P/Cld2FsDuG6G+/cOXvx1LneFCHBcuyuHpNWIuz4l9d2z6W6qvF1VLa+qbYELgccB3wP+tB2rtgXdff0AfgYsS3LHrtAkD5xi3t/nzmPe9gG+M8U4UzkSeB9wQVVd2u5leWGS57TlJclDVuI1Hge8JMlabfpNJw0/CXhMkvu24Xdv9wyWtMQZ1CT13fOBL03q94XW/wvApcDZwH8AP6K7ifrv6ALeu5L8L3AG3a7JyV4G7JfkTLpj3A4asqajgAey4tme+wD7t+X9hO64umF9HPg5cGab/s8HB1bVVcCL6Haznkm323PBL1MiafS8hZSksZZkg6q6MclmwA/p7sl5l/v8SdI48hg1SePuP5NsTHeA/dsMaZKWEreoSZIk9ZTHqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSeur/B2g6gzZH312VAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_aeaONln-Qpd"
      },
      "source": [
        "##Claims and Fraud done based on Age of Vehicle"
      ],
      "id": "_aeaONln-Qpd"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tsXm-j9X-uqp",
        "outputId": "1c6eed06-4263-461a-a619-1cef03b10adf"
      },
      "source": [
        "customers['AgeOfVehicle'].value_counts()"
      ],
      "id": "tsXm-j9X-uqp",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7 years        4388\n",
              "more than 7    2993\n",
              "6 years        2581\n",
              "5 years         990\n",
              "new             285\n",
              "4 years         176\n",
              "3 years         100\n",
              "2 years          52\n",
              "Name: AgeOfVehicle, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VaIAbCYH-v8W",
        "outputId": "802408bb-95ef-438c-e270-4375cba47008"
      },
      "source": [
        "customers[customers['FraudFound_P']==1]['AgeOfVehicle'].value_counts()"
      ],
      "id": "VaIAbCYH-v8W",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7 years        260\n",
              "6 years        165\n",
              "more than 7    148\n",
              "5 years         60\n",
              "new             26\n",
              "4 years         15\n",
              "3 years          9\n",
              "2 years          2\n",
              "Name: AgeOfVehicle, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "9401f469",
        "outputId": "b51b1b0c-5c6a-448d-97f1-4f3fa644198b"
      },
      "source": [
        "plt.subplots(figsize=(10, 6))\n",
        "\n",
        "sns.countplot(x='AgeOfVehicle',\n",
        "              data=customers,\n",
        "              palette='RdYlGn',\n",
        "              order=customers['AgeOfVehicle'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Claim')\n",
        "plt.xlabel('Age of Vehicle')\n",
        "plt.title('Age of Vehicle  vs Number of Claim Done')\n",
        "\n",
        "plt.show()"
      ],
      "id": "9401f469",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "441833c2",
        "outputId": "edf6e9e4-f2d1-4287-e48c-9239588b2473"
      },
      "source": [
        "plt.subplots(figsize=(10, 6))\n",
        "\n",
        "sns.countplot(x='AgeOfVehicle',\n",
        "              data=customers[customers['FraudFound_P']==1],\n",
        "              palette='RdYlGn',\n",
        "              order=customers['AgeOfVehicle'].value_counts().index)\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.ylabel('Number of Fraud')\n",
        "plt.xlabel('Age of Vehicle')\n",
        "plt.title('Age of Vehicle  vs Number of Fraud')\n",
        "\n",
        "plt.show()"
      ],
      "id": "441833c2",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqbG15lqR5Aag5lpfVfceSUWSJEkTZs5AVlUbACR5O3AV8CkgwP7AFotSnSRJ0gQYZspyr6r6UFXdUFXXV9WHgb1HXZgkSdKkGCaQ3Zhk/yRrJlkjyf7AjaMuTJIkaVIME8j+HNgHuKa/vahvkyRJ0gKY8xiyKVV1CU5RSpIkjcxKA1mSjzPL2ZZV9dKRVCQ18Mtv/n3rEkbmPk95S+sSJEkrsdJABnxlYPmewPOBK0dTjiRJ0uQZZsry84P3kxwFfHdkFUmSJE2Y1blS/w7AfRe6EEmSpEk1zDFkU1fsT//zauBvRlyXJEnSxBhmynKDxShEkiRpUg1zUD9JNqabqrznVFtVfXtURUmSJE2SYaYsXwYcCmwNnAXsDnwfeMpoS5MkSZoMwxzUfyjwaODSqnoy8AjgupFWJUmSNEGGCWQ3VdVNAEnWqaqfAA8cbVmSJEmTY5hjyC5PshHwReDEJL8CLh1tWZIkSZNjmLMsn98vvi3JycCGwNdGWpUkSdIEmTeQJVkTOLeqHgRQVacsSlWSJEkTZN5jyKrqNuCCJNsuUj2SJEkTZ5iD+jcGzk1yUpLjp24r2yjJx5L8PMk5A21vS3JFkrP6254D696Q5MIkFyR5xuq9HUmSpKVnzinLJGtV1a3Am1fzuT8B/BPwyWnt76uq9057rZ2AfYGHAFsC30iyYz9CJ0mSdLc23wjZj+CO48ZeWFWnDN5W9sT9lfyvHbKOvYGjq+rmqvoZcCGw25DbSpIkLWnzBbIMLO+xgK/5yiRn91OaG/dtWwGXDTzm8r5NkiTpbm++QFYjeL0PA9sDuwBXAYet6hMkOSTJ8iTLV6xYsdD1SZIkLbr5LnvxoCRn042Ubd8v09+vqtp5VV+sqq6ZWk7yEeAr/d0rgG0GHrp13zbbcxwBHAGw6667jiI0SpIkLar5AtmDF/rFkmxRVVf1d58PTJ2BeTzwmSSH0x3UvwP9MWxaWJe+48DWJYzUdm86snUJkiStsjkDWVX9QV+PlOQo4EnApkkuB94KPCnJLnTToZcAL+9f69wkxwDnAbcCr/AMS0mSNCmG+S7L1VJV+83S/NF5Hv9O4J2jqkeSJGlcDXNhWEmSJI3QnIEsyUn9z3cvXjmSJEmTZ74pyy2SPA7YK8nR3PW6ZFTVGSOtTJIkaULMF8jeQve1SVsDh09bV8BTRlWUJEnSJJnvLMvPAZ9L8uaqevsi1iRJkjRRVnqWZVW9PclewBP6pm9V1Vfm20aSJEnDW+lZlkneBRxKd42w84BDk/yfURcmSZI0KYa5DtmzgV2q6naAJEcCZwJvHGVhkiRJk2LY65BtNLC84SgKkSRJmlTDjJC9Czgzycl0l754AvD6kVYlSZI0QYY5qP+oJN8CHt03/U1VXT3SqiRJkibIUN9lWVVXAcePuBZJkqSJ5HdZSpIkNWYgkyRJamzeQJZkzSQ/WaxiJEmSJtG8gayqbgMuSLLtItUjSZI0cYY5qH9j4NwkPwJunGqsqr1GVpUkSdIEGSaQvXnkVUiSJE2wYa5DdkqS7YAdquobSe4FrDn60iRJkibDMF8u/pfA54B/6Zu2Ar44yqIkSZImyTCXvXgFsAdwPUBV/RS47yiLkiRJmiTDBLKbq+r3U3eSrAXU6EqSJEmaLMMEslOSvBFYN8nTgGOBL4+2LEmSpMkxTCB7PbAC+DHwcuAE4E2jLEqSJGmSDHOW5e1JjgR+SDdVeUFVOWUpSZK0QFYayJI8G/hn4CIgwP2SvLyq/n3UxUmSJE2CYS4Mexjw5Kq6ECDJ9sBXAQOZJEnSAhjmGLIbpsJY72LghhHVI0mSNHHmHCFL8oJ+cXmSE4Bj6I4hexFw2iLUJkmSNBHmm7J87sDyNcAT++UVwLojq0iSJGnCzBnIquqgxSxEkiRpUg1zluX9gFcBywYfX1V7ja4sSZKkyTHMWZZfBD5Kd3X+20dbjiRJ0uQZJpDdVFUfGHklkiRJE2qYQPb+JG8Fvg7cPNVYVWeMrCpJkqQJMkwgexhwAPAU7pyyrP6+JEmS/kDDBLIXAfevqt+PuhhJkqRJNMyV+s8BNhp1IZIkSZNqmBGyjYCfJDmNux5D5mUvJEmSFsAwgeytI69CkiRpgq00kFXVKYtRiCRJ0qQa5kr9N9CdVQlwD2Bt4MaquvcoC5MkSZoUw4yQbTC1nCTA3sDuoyxKkiRpkgxzluUdqvNF4BkjqkeSJGniDDNl+YKBu2sAuwI3jawiSZKkCTPMWZbPHVi+FbiEbtpSkiRJC2CYY8gOWoxCJEmSJtWcgSzJW+bZrqrq7SOoR5IkaeLMN0J24yxt6wEHA/cBDGSSJEkLYM5AVlWHTS0n2QA4FDgIOBo4bK7tJEmStGrmPYYsySbAa4D9gSOBR1bVrxajMEmSpEkx53XIkrwHOA24AXhYVb1tVcJYko8l+XmScwbaNklyYpKf9j837tuT5ANJLkxydpJH/gHvSZIkaUmZ78KwrwW2BN4EXJnk+v52Q5Lrh3juTwDPnNb2euCkqtoBOKm/D/AsYIf+dgjw4eHfgiRJ0tI2ZyCrqjWqat2q2qCq7j1w22CY77Gsqm8D105r3ptu6pP+5/MG2j/ZfxPAD4CNkmyx6m9HkiRp6Vmlr05aAJtX1VX98tXA5v3yVsBlA4+7vG+bIckhSZYnWb5ixYrRVSpJkrRIFjuQ3aGqCqjV2O6Iqtq1qnbdbLPNRlCZJEnS4lrsQHbN1FRk//PnffsVwDYDj9u6b5MkSbrbW+xAdjxwYL98IPClgfYX92db7g78emBqU5Ik6W5tmC8XXy1JjgKeBGya5HLgrcA/AMckORi4FNinf/gJwJ7AhcBv6S5AK0mSNBFGFsiqar85Vj11lscW8IpR1SJJkjTOmh3UL0mSpI6BTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpsbVaFyBpPP3mqi+0LmGk1t/iBa1LkKQ7OEImSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJaqzJhWGTXALcANwG3FpVuybZBPgssAy4BNinqn7Voj5JkqTF1HKE7MlVtUtV7drffz1wUlXtAJzU35ckSbrbG6cpy72BI/vlI4HnNaxFkiRp0bQKZAV8PcnpSQ7p2zavqqv65auBzWfbMMkhSZYnWb5ixYrFqFWSJGmkWn25+OOr6ook9wVOTPKTwZVVVUlqtg2r6gjgCIBdd9111sdIkiQtJU1GyKrqiv7nz4HjgN2Aa5JsAdD//HmL2iRJkhbbogeyJOsl2WBqGXg6cA5wPHBg/7ADgS8tdm2SJEkttJiy3Bw4LsnU63+mqr6W5DTgmCQHA5cC+zSoTZIkadEteiCrqouBh8/S/kvgqYtdjyRJUmvjdNkLSZKkiWQgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJja3VugBJWkou+8XnW5cwUtts+qetS5AmkiNkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMLxeXJP3BvnrO21uXMFLPfuibW5eguzlHyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjXvZCkqQRedPX/lfrEkbmHc98X+sS7lYcIZMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNjV0gS/LMJBckuTDJ61vXI0mSNGpjdR2yJGsC/w94GnA5cFqS46vqvLaVSZKkhfDIw/ZpXcJInfHaY1Zru3EbIdsNuLCqLq6q3wNHA3s3rkmSJGmkxi2QbQVcNnD/8r5NkiTpbitV1bqGOyR5IfDMqnpZf/8A4DFV9cqBxxwCHNLffSBwwaIXOrtNgV+0LmIM2S+zs19msk9mZ7/Mzn6Znf0y0zj1yXZVtdlsK8bqGDLgCmCbgftb9213qKojgCMWs6hhJFleVbu2rmPc2C+zs19msk9mZ7/Mzn6Znf0y01Lpk3GbsjwN2CHJ/ZLcA9gXOL5xTZIkSSM1ViNkVXVrklcC/wGsCXysqs5tXJYkSdJIjVUgA6iqE4ATWtexGsZuGnVM2C+zs19msk9mZ7/Mzn6Znf0y05Lok7E6qF+SJGkSjdsxZJIkSRPHQCZJktSYgWw1Jdk+yTr98pOSvDrJRq3ramWS3/sw3F9msk+0KpKsl2SNfnnHJHslWbt1Xa3ZLzMt1c8WA9nq+zxwW5IH0B0wuA3wmbYlNfWLJN9IcvBS2PEbcH+ZyT6ZQ5LtW9cwhr4N3DPJVsDXgQOATzStaDzYLzMtyc8WA9nqu72qbgWeD3ywql4HbNG4ppbOB/4ReApwUZIvJdk3ybqN6xoX7i8z2Sdz+1iSi5IcneQVSR7WuqAxkKr6LfAC4ENV9SLgIY1rGgf2y0xL8rPFQLb6bkmyH3Ag8JW+bZKHiW+pqq9U1f5037DwaWAf4PIkY/8/k0Xg/jKTfTKHqnoi8GDgg8BGwFeTXNu2quaS5LHA/sBX+7Y1G9YzLuyXmZbkZ8vYXYdsCTkI+B/AO6vqZ0nuB3yqcU0tZWqhqn4HHAMck2RD4HnNqhof7i8z2SdzSPJ44I/720Z0f1S+07So9g4F3gAcV1XnJrk/cHLjmsaB/TLTkvxs8TpkqyHJmsAn+9EgAUn+uqre27qOceT+MpN9Mr8ktwKnA+8CTqiq3zcuqal+f3l3Vf1161rGif0y01L+bHHKcjVU1W3Adv33bQowjM3N/WUm+2SlNgX+Hngs8LX+hJm3N66pmX5/eXzrOsaN/TLTUv5sccpy9V0MfC/J8cCNU41VdXi7kjTG3F9msk/mUFXXJbmY7uywrYHHsQSOgRmxM/t95Vjuur98oV1JY8F+mWlJfrYYyFbfRf1tDWCDxrVo/Lm/zGSfzKEPYz8Bvgt8GDho0qctgXsCv6Q7k3tKAZMcPMB+mc2S/GzxGDJJGjNJ1qiq21vXIWnxOEK2mpJsBvxvuuu93HOqvaqeMudGE6Dvl78EljGwf1XVS1vVNA7cX2ayT+b1gCQfBjavqocm2RnYq6re0bqwVpLcEziYmfvLpH+22C/TLNXPFg/qX32fpptSuB/wd8AlwGktCxoTXwI2BL5Bd02cqdukc3+ZyT6Z20foLmVwC0BVnQ3s27Si9j4F/BHwDOAUumPrbmha0XiwX2Zakp8tTlmupiSnV9WjkpxdVTv3badV1aNb19ZSkrOqapfWdYwb95eZ7JO5TfVDkjOr6hF920T/25rqi6n9pf++xu9U1e6ta2vJfplpqX62OGW5+m7pf16V5NnAlcAmDesZF19JsmdVndC6kDHj/jKTfTK3X/TfZ1kASV4IXNW2pOam9pfrkjwUuBq4b8N6xoX9MtOS/GwxkK2+d/RXoX8t3deb3Bv4X21LGguHAm9McjPdP4oAVVX3bltWc+4vM9knc3sF3ZciPyjJFcDP6L4aZ5IdkWRj4M3A8cD6wFvaljQW7JeZluRni1OWkjRmkko/2WkAAAtPSURBVKwDvJDu5JhNgOvp/mPz9y3rkjQ6HtS/mpLsmOSkJOf093dO8qbWdY2DJBsn2S3JE6ZurWtqzf1lJvtkXl8Cnks3ynwl8BsGLnA5iZJsnuSjSf69v79TkoNb19Wa/TLTUv1scYRsNSU5BXgd8C8DB92eU1UPbVtZW0leRjdtuTVwFrA78P1xP9141NxfZrJP5mY/zNQHjo8Df1tVD0+yFnBmVT2scWlN2S8zLdXPFkfIVt+9qupH09pubVLJeDkUeDRwaVU9GXgEcF3bksaC+8tM9sncTk0ysX9Q57BpVR0D3A5QVbcCt7UtaSzYLzMtyc8WD+pffZ4FNbubquqmJCRZp6p+kuSBrYsaA+4vM9knc3s88JIkPwNu5s6TY3ZuW1ZTNya5D3fuL7sDv25b0liwX2Zakp8tBrLV51lQs7s8yUbAF4ETk/wKuLRxTePA/WUm+2Ruz2pdwBh6Ld1ZhNsn+R6wGd2JD5POfplpSX62eAzZakqyZlXdlmQ9YI2qmvQrI8+Q5Il0V+3/2qR/MbL7y0z2iVZVf3zUA+lGDC+oqltWsslEsF/uaql+tngM2er7aZL3ANsulV/2YkmyZpIt6f5Xchbd13pMOveXmewTDS3J6cAhwJVVdc6kh44p9susluRni4Fs9T0c+C/go0l+kOSQJJN+8VOSvAq4BjiRO7/H8itNixoP7i8z2SdaFX8GbAWcluToJM9IktZFjQH7ZaYl+dnilOUC6KfmPgNsBHwOeHtVXdi2qjaSXAg8pqp+2bqWceX+MpN9omElWQN4DvBhurMJPw68v6qubVpYY/bL7JbSZ4sjZKupn5bbK8lxwD8ChwH3B74MTPL3OF6GZ/jM4P4yk32iVZVkZ7r95D3A54EX0X2LwTdb1tWa/XJXS/WzxbMsV99PgZOB91TVqQPtn5vEK9MneU2/eDHwrSRfpTtdH4CqOrxJYePD/WUm+0RD64+Vug74KPD6qpr6fPlhkj3aVdaW/TKrJfnZ4pTlakqyflX9pnUd4yLJW+dZPfHfwef+MpN9olWR5P5VdXHrOsaN/TLTUv1sMZBpQSV5UVUdu7I2SZJ0JwOZFlSSM6rqkStrkyRJd/IYMi2IJM8C9gS2SvKBgVX3Zgl8h5gkSS15luUqSvLqJNu0rmMMXQksB24CTh+4HQ88o2FdTSV5zNT1b5Ksm+Tvknw5ybuTbNi6vnGQ5PFJXpPk6a1r0dKQ5JOtaxgHSXZL8uh+eaf+39GeretqKcmDkjw1yfrT2p/ZqqZhOWW5ipL8GrgRuAg4Cji2qla0rWp8JFnbK0XfKcm5wMOr6tYkRwC/pbsWzlP79hc0LbCBJD+qqt365b+k+96544CnA1+uqn9oWZ/GS5LjpzcBT6a/pENV7bXoRY2B/kSqZ9HNdJ0IPIbuzMKnAf9RVe9sWF4TSV5N93lyPrALcGhVfalfN/aHzhjIVlGSM4FHAX9Cd4XkvehGgo4CvrCUvqZBo5fk/Kp6cL98lw+EJGdV1S7tqmsjyZlV9Yh++TRgz6pa0X/v3A+q6mFtK9Q4SXIGcB7wr0DRBbKjgH0BquqUdtW1k+THdKFjHeBqYOuquj7JusAPq2rnpgU20PfJY6vqN0mW0f3n91NV9f7Bz51x5ZTlqququr2qvl5VBwNbAh8Cnkl3DS5p0DlJDuqX/zPJrgBJdgQmdSRxjSQbJ7kP3X8KVwBU1Y14vKFm2pXuP71/C/y6qr4F/K6qTpnUMNa7tapuq6rfAhdV1fUAVfU74Pa2pTWzxtTlLqrqEuBJwLOSHE4X5MeagWzV3eWXWlW3VNXxVbUfsF2jmsZOknu1rmFMvAx4YpKLgJ2A7ye5GPhIv24SbUj3B3Y5sEmSLaC7dhBL4ENTi6v/D/D7gIOAv03yT3hCGsDvBz5nHzXV2B+bOqmB7Jokd8w69OHsOcCmwNiPvDtluYqS7FhV/9W6jnGV5HF0UwvrV9W2SR4OvLyq/mfj0prqD+y/H90fksur6prGJY2d/o/L5lX1s9a1aHwleTawR1W9sXUtLSVZZ+Cq/IPtmwJbVNWPG5TVVJKt6UYOr55l3R5V9b0GZQ3NQKYFleSHwAuB4weOEzqnqh7atjJJksaXU5ZacFV12bSm25oUIknSEuE8vBbaZf20ZSVZGziU7hRkSZI0B6cstaD64xfeT3dZkABfp7sWzC+bFiZJ0hgzkGnBJFkT+GRV7d+6FkmSlhKPIdOCqarbgO2S3KN1LZIkLSUeQ6aFdjHwvf7rTm6caqyqw9uVJEnSeDOQaaFd1N/WADZoXIskSUuCx5BpJPqrrk9dKVmSJM3DY8i0oJI8tP8C9nOBc5OcnuQhreuSJGmcGci00I4AXlNV21XVdsBr6b63UZIkzcFApoW2XlWdPHWnqr4FrNeuHEmSxp8H9WuhXZzkzcCn+vt/QXfmpSRJmoMjZFpoLwU2A77Q3zbr2yRJ0hw8y1KSJKkxpyy1oJLsCrwRWMbA/lVVO7eqSZKkcecImRZUkguA1wE/Bm6faq+qS5sVJUnSmHOETAttRVUd37oISZKWEkfItKCSPBXYDzgJuHmqvaq+0KwoSZLGnCNkWmgHAQ8C1ubOKcuiO+NSkiTNwhEyLagkF1TVA1vXIUnSUuJ1yLTQTk2yU+siJElaShwh04JKcj6wPfAzumPIApSXvZAkaW4GMi2oJNvN1u5lLyRJmpuBTJIkqTGPIZMkSWrMQCZJktSYgUzSWEnyvCSV5EGL8FqbJflhkjOT/PFA+1uTvGvaY3fpT1qZ67k+keSFs7RvmeRzK6njW/33wEqaUAYySeNmP+C7/c9Reyrw46p6RFV9Z6D9KODPpj123759lVTVlVU1I6hJ0iADmaSxkWR94PHAwXQBaKp9jSQfSvKTJCcmOWFqNCrJo5KckuT0JP+RZItZnndZkm8mOTvJSUm2TbIL8H+BvZOclWTdqcdX1X8Bv0rymIGn2Qc4Ksn2Sb7Wv953po3kPSHJqUkuHqhvWZJz+uU1k7w3yTl9La+apdanJ/l+kjOSHNv3iaS7OQOZpHGyN/C1PhD9Msmj+vYXAMuAnYADgMcCJFkb+CDwwqp6FPAx4J2zPO8HgSP76+F9GvhAVZ0FvAX4bFXtUlW/m7bNUfShMMnuwLVV9VPgCOBV/ev9NfChgW22oAuUzwH+YZY6Dunfxy4DtdwhyabAm4A/qapHAsuB18zeVZLuTvwuS0njZD/g/f3y0f390+lCzrFVdTtwdZKT+8c8EHgocGISgDWBq2Z53sfShTqAT9GNjK3MZ+m+eeK19NOV/WjV44Bj+9cDWGdgmy/2NZ6XZPNZnvNPgH+uqlsBquraaet3pwud3+uf/x7A94eoVdISZyCTNBaSbAI8BXhYkqILV5XkdfNtBpxbVY9d6Hqq6rIkPwOeCPwpXahbA7iuqnaZY7Obp9W2qgKcWFWLcfycpDHilKWkcfFC4FNVtV1VLauqbei+guuPge8Bf9ofS7Y58KR+mwuAzZLcMYWZ5CGzPPep3HlM2v7Ad2Z5zGyOAt4HXFxVl1fV9cDPkryof70kefgqvMcTgZcnWavffpNp638A7JHkAf369ZLsuArPL2mJMpBJGhf7AcdNa/t83/554HLgPODfgDOAX1fV7+mC3LuT/CdwFt2U4nSvAg5KcjbdMWiHDlnTscBDuOvZlfsDB/evdy7dcW/D+lfgv4Gz++3/fHBlVa0AXkI3PXo23XTlyC//Iak9vzpJ0pKQZP2q+k2S+wA/Avaoqqtb1yVJC8FjyCQtFV9JshHdge5vN4xJujtxhEySJKkxjyGTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjf1/I5Qf+bKK3kEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19885962"
      },
      "source": [
        "daydict = {\n",
        "       1 : 'Monday',\n",
        "       2 : 'Tuesday',\n",
        "       3 : 'Wednesday',\n",
        "       4 : 'Thursday',\n",
        "       5 : 'Friday',\n",
        "       6 : 'Saturday',\n",
        "       7 : 'Sunday'\n",
        "}"
      ],
      "id": "19885962",
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "321859bc"
      },
      "source": [
        "def parse_date(year, month_name, week_month, day):\n",
        "    month = month_name\n",
        "    first_date = datetime(year, month, 1)\n",
        "    first_day = first_date.isoweekday()\n",
        "    first_week_number = first_date.isocalendar()[1]\n",
        "    if (first_week_number==52):\n",
        "        first_week_number = 1\n",
        "    week_number = first_week_number + week_month - 1\n",
        "    string_date = str(year) + '-' + str(week_number) + '-' + daydict.get(day)\n",
        "    date = datetime.strptime(string_date, '%Y-%U-%A')\n",
        "    return date"
      ],
      "id": "321859bc",
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5d562b9"
      },
      "source": [
        "def convert_month(month):\n",
        "    \n",
        "    if month=='Jan':\n",
        "        return 1\n",
        "    elif month=='Feb':\n",
        "        return 2\n",
        "    elif month=='Mar':\n",
        "        return 3\n",
        "    elif month=='Apr':\n",
        "        return 4\n",
        "    elif month=='May':\n",
        "        return 5\n",
        "    elif month=='Jun':\n",
        "        return 6\n",
        "    elif month=='Jul':\n",
        "        return 7\n",
        "    elif month=='Aug':\n",
        "        return 8\n",
        "    elif month=='Sep':\n",
        "        return 9\n",
        "    elif month=='Oct':\n",
        "        return 10\n",
        "    elif month=='Nov':\n",
        "        return 11\n",
        "    elif month=='Dec':\n",
        "        return 12\n",
        "    \n",
        "customers['Month'] = customers['Month'].apply(convert_month)\n",
        "customers['MonthClaimed'] = customers['MonthClaimed'].apply(convert_month)  "
      ],
      "id": "d5d562b9",
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ec1b90a"
      },
      "source": [
        "def convert_day(day):\n",
        "    \n",
        "    if day=='Monday':\n",
        "        return 1\n",
        "    elif day=='Tuesday':\n",
        "        return 2\n",
        "    elif day=='Wednesday':\n",
        "        return 3\n",
        "    elif day=='Thursday':\n",
        "        return 4\n",
        "    elif day=='Friday':\n",
        "        return 5\n",
        "    elif day=='Saturday':\n",
        "        return 6\n",
        "    elif day=='Sunday':\n",
        "        return 7\n",
        "\n",
        "customers['DayOfWeek'] = customers['DayOfWeek'].apply(convert_day)\n",
        "customers['DayOfWeekClaimed'] = customers['DayOfWeekClaimed'].apply(convert_day)   "
      ],
      "id": "7ec1b90a",
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9eba3e8",
        "outputId": "7f111a9f-10bf-4ad4-f56c-c9f524f96b86"
      },
      "source": [
        "customers['Month'].value_counts()"
      ],
      "id": "a9eba3e8",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     1056\n",
              "5     1028\n",
              "3      995\n",
              "6      981\n",
              "10     973\n",
              "12     970\n",
              "4      959\n",
              "9      952\n",
              "2      949\n",
              "7      938\n",
              "11     913\n",
              "8      851\n",
              "Name: Month, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "9620dec7",
        "outputId": "08cb68d4-964c-46bf-f616-63447b3cc7bb"
      },
      "source": [
        "customers[customers['DayOfWeekClaimed'].isna()]"
      ],
      "id": "9620dec7",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>WeekOfMonth</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>Make</th>\n",
              "      <th>AccidentArea</th>\n",
              "      <th>DayOfWeekClaimed</th>\n",
              "      <th>MonthClaimed</th>\n",
              "      <th>WeekOfMonthClaimed</th>\n",
              "      <th>Sex</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fault</th>\n",
              "      <th>PolicyType</th>\n",
              "      <th>VehicleCategory</th>\n",
              "      <th>VehiclePrice</th>\n",
              "      <th>FraudFound_P</th>\n",
              "      <th>PolicyNumber</th>\n",
              "      <th>RepNumber</th>\n",
              "      <th>Deductible</th>\n",
              "      <th>DriverRating</th>\n",
              "      <th>Days_Policy_Accident</th>\n",
              "      <th>Days_Policy_Claim</th>\n",
              "      <th>PastNumberOfClaims</th>\n",
              "      <th>AgeOfVehicle</th>\n",
              "      <th>AgeOfPolicyHolder</th>\n",
              "      <th>PoliceReportFiled</th>\n",
              "      <th>WitnessPresent</th>\n",
              "      <th>AgentType</th>\n",
              "      <th>NumberOfSuppliments</th>\n",
              "      <th>AddressChange_Claim</th>\n",
              "      <th>NumberOfCars</th>\n",
              "      <th>Year</th>\n",
              "      <th>BasePolicy</th>\n",
              "      <th>ClaimSize</th>\n",
              "      <th>Claim_Amount_Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1146</th>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Rural</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Policy Holder</td>\n",
              "      <td>Sedan - All Perils</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>more than 69000</td>\n",
              "      <td>0</td>\n",
              "      <td>1517</td>\n",
              "      <td>15</td>\n",
              "      <td>400</td>\n",
              "      <td>2.0</td>\n",
              "      <td>more than 30</td>\n",
              "      <td>none</td>\n",
              "      <td>none</td>\n",
              "      <td>new</td>\n",
              "      <td>16 to 17</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>External</td>\n",
              "      <td>none</td>\n",
              "      <td>no change</td>\n",
              "      <td>1 vehicle</td>\n",
              "      <td>1994</td>\n",
              "      <td>All Perils</td>\n",
              "      <td>70530.646951</td>\n",
              "      <td>70.530647</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Month  WeekOfMonth  ...     ClaimSize Claim_Amount_Percent\n",
              "1146      7            2  ...  70530.646951            70.530647\n",
              "\n",
              "[1 rows x 35 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "853a89a9"
      },
      "source": [
        "customers['DayOfWeekClaimed'].fillna(1, inplace=True)"
      ],
      "id": "853a89a9",
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bcc92d7",
        "outputId": "38df96d9-4b05-4f9f-b276-b9a2f4817c51"
      },
      "source": [
        "customers.isna().sum()"
      ],
      "id": "0bcc92d7",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month                   0\n",
              "WeekOfMonth             0\n",
              "DayOfWeek               0\n",
              "Make                    0\n",
              "AccidentArea            0\n",
              "DayOfWeekClaimed        0\n",
              "MonthClaimed            1\n",
              "WeekOfMonthClaimed      0\n",
              "Sex                     0\n",
              "MaritalStatus           0\n",
              "Age                     5\n",
              "Fault                   0\n",
              "PolicyType              0\n",
              "VehicleCategory         0\n",
              "VehiclePrice            0\n",
              "FraudFound_P            0\n",
              "PolicyNumber            0\n",
              "RepNumber               0\n",
              "Deductible              0\n",
              "DriverRating            6\n",
              "Days_Policy_Accident    0\n",
              "Days_Policy_Claim       0\n",
              "PastNumberOfClaims      0\n",
              "AgeOfVehicle            0\n",
              "AgeOfPolicyHolder       0\n",
              "PoliceReportFiled       0\n",
              "WitnessPresent          0\n",
              "AgentType               0\n",
              "NumberOfSuppliments     0\n",
              "AddressChange_Claim     0\n",
              "NumberOfCars            0\n",
              "Year                    0\n",
              "BasePolicy              0\n",
              "ClaimSize               0\n",
              "Claim_Amount_Percent    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f086f209"
      },
      "source": [
        "customers['MonthClaimed'].fillna(7, inplace=True)"
      ],
      "id": "f086f209",
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5ae15d1"
      },
      "source": [
        "from datetime import datetime, timedelta\n",
        "gap = []\n",
        "accident_dates = []\n",
        "claim_dates = []\n",
        "\n",
        "for index, row in customers.iterrows():\n",
        "    accident_year = row['Year']\n",
        "    claim_year = accident_year if (row['Month'] <= int(row['MonthClaimed'])) else accident_year + 1\n",
        "    accident_date = parse_date(accident_year, row['Month'], row['WeekOfMonth'], row['DayOfWeek'])\n",
        "    claim_date = parse_date(claim_year, int(row['MonthClaimed']), row['WeekOfMonthClaimed'], row['DayOfWeekClaimed'])\n",
        "    accident_dates.append(accident_date)\n",
        "    claim_dates.append(claim_date)\n",
        "    gap.append(abs((claim_date - accident_date).days))"
      ],
      "id": "e5ae15d1",
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "767e356c"
      },
      "source": [
        "customers['Claim_Gap'] = gap"
      ],
      "id": "767e356c",
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68fc000c",
        "outputId": "61493347-0b50-4843-a1a4-8bb3bc81a44b"
      },
      "source": [
        "customers[customers['WitnessPresent']=='Yes'].shape"
      ],
      "id": "68fc000c",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(68, 36)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1263c0e8",
        "outputId": "a2524d0c-138a-4484-aee8-a9b94971dd12"
      },
      "source": [
        "claim_gap_10 = customers[customers['Claim_Gap']>10]\n",
        "print(claim_gap_10.shape)\n",
        "print(claim_gap_10[claim_gap_10['FraudFound_P']==1].shape)"
      ],
      "id": "1263c0e8",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2803, 36)\n",
            "(222, 36)\n"
          ]
        }
      ]
    }
  ]
}